SOTA LLMs for podcasts

2025-12-03 12:05 UTC gpt-5-1-thinking Open in ChatGPT ↗

You

What is the current SOTA regarding LLMs and what is easier/cheaper to implement on a custom project. It also needs to be reliable though. The content I am interested is to abstract a given cooking recipe and generate an audio version of it that will exist in a podcast like format targeting neurodiverse people. I want an LLM to write the main script for that that will then go into a TTS model. Probably ElevenLabs

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Short version: for your “recipe → neurodiversity-friendly podcast script → ElevenLabs” idea, you don’t need the absolute craziest SOTA model. A good “mini” frontier model via API is probably the sweet spot: cheap, simple to integrate, and more than good enough in quality.

I’ll break it into:

1. What “SOTA” actually looks like right now
2. What you actually need (and don’t need)
3. Concrete architecture & prompts for your project
4. Specific model recommendations (from easiest → cheapest)

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1. Quick snapshot of SOTA LLMs (2025)

Very high level:

• Frontier closed models (API only)

  • OpenAI: GPT-5.1, o3, GPT-4.1 family. 4.1 is their “smartest non-reasoning general model” with big context and strong instruction following. 4.1 mini/nano are cheaper variants. citeturn0search1turn0search8turn0search14
  • OpenAI o3 / o4-mini: focused on very reliable reasoning; aimed at tasks needing careful thinking, with o3-pro available via API. citeturn0search5
  • Anthropic Claude 3.5 Sonnet / Opus: extremely good at nuanced writing, tone, and long-form content; Sonnet is positioned as a fast, cost-effective “sweet spot” and hits SOTA benchmarks on reasoning and coding. citeturn0search2turn0search6
  • Google Gemini 2.x / 3.x, Amazon Nova, etc. – competitive in benchmarks; you’d mostly care about them if you’re deep in those cloud ecosystems. citeturn0news36turn0news39

• Open-source SOTA

  • Llama 3.1 405B (and 8B/70B) – Meta’s flagship; 405B rivals top closed models, smaller variants are good for self-hosting. citeturn0search3turn0search15
  • A bunch of others (Mistral, Kimi-Dev-72B, etc.) reach or surpass GPT-4-ish levels on some benchmarks. citeturn0search10turn0search13

In short: there are tons of crazy-powerful options. But your use-case is structured NLG for recipes, not theorem proving. That matters a lot.

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2. What you actually need for “recipe → podcast script”

Your pipeline is roughly:

Recipe text → abstract it → rewrite as a podcast-style script for neurodiverse listeners → send to ElevenLabs for TTS.

For this, the model mainly needs:

1. Strong instruction following

   • Follow a template (“sections”, “tone”, “length”).
   • Not forget ingredients or steps.

2. Good controllable style

   • Clear, simple language.
   • Optional “deep dive” explanations.
   • Sensory-friendly wording (e.g., not overly metaphorical, explicit time/temperature).

3. Reliability / consistency

   • Every ingredient used in some step.
   • Step order preserved logically.
   • Stable format so your code can parse it.

All of the top frontier models (and many small ones) can do this reliably if you design the prompts & checks correctly. You don’t need o3-level heavyweight reasoning; a good “mini” model is enough.

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3. Recommended architecture for your project

Here’s a very practical design that balances ease, cost, and reliability:

Step 1 – Normalize the recipe

Input: URL, plain text, or structured recipe.

• Use your own code to parse or just send raw recipe text to the LLM with:
  • “Extract to JSON” prompt → get:

    {
      "title": "...",
      "servings": 4,
      "ingredients": [
        {"name": "olive oil", "amount": "2 tbsp"},
        ...
      ],
      "steps": [
        {"step_number": 1, "text": "...", "estimated_minutes": 5},
        ...
      ],
      "notes": [...]
    }

• This makes later steps much more reliable, because you separate understanding from writing style.

Step 2 – Validate & fix

• Programmatically check:
  • Every ingredient is referenced in at least one step.
  • No obviously missing times/temps.
• If something is off, send both the recipe and the JSON back to the LLM with a prompt like:

  “You produced the following JSON but it has these issues: [description]. Fix the JSON only.”

This is cheap and massively increases reliability.

Step 3 – Generate the neurodiversity-friendly podcast script

From the validated JSON, call the LLM again with a strong template, e.g.:

• Sections you might want:

  1. Intro (what this dish is + who it’s for)
  2. What you’ll need (equipment + ingredients, clearly enumerated)
  3. Step-by-step walk-through, with:
     • One clear instruction per step
     • Approx. timing (“This will take about 5 minutes.”)
     • Pauses/cue words for TTS (“[Pause]”, “[Sound: chopping]” if you want)
  4. Recap (short summary of steps)
  5. Optional variations / sensory notes, clearly marked as “optional”.

• Neurodiversity-friendly style guidelines baked into the prompt:

  • Short sentences.
  • Avoid idioms and sarcasm.
  • Explicit transitions: “Next”, “Now”, “Finally”.
  • Reassurance: “It’s okay if it’s not perfect; look for these signs instead…”.

You can ask the LLM to output something like:

{
  "tone": "calm_supportive",
  "sections": [
    {"name": "intro", "script": "..."},
    {"name": "ingredients", "script": "..."},
    {"name": "steps", "script": "..."},
    {"name": "recap", "script": "..."}
  ]
}

Then you stitch sections[*].script together for ElevenLabs.

Step 4 – Final QA

Before sending to TTS, you can:

• Run a quick LLM “checker” prompt:

  “Here is the original recipe JSON and the final script. List any ingredients or steps that are missing or misrepresented. If none, answer: OK.”

• If it reports issues, you either:
  • Auto-fix via another call, or
  • Flag it for human review if this is a premium/high-stakes feed.

Step 5 – ElevenLabs TTS

• Feed the final script to ElevenLabs:
  • Choose a steady, neutral, clear voice profile.
  • Consider:
    • Slightly slower speech rate
    • Mild prosody variation (not monotone, but not chaotic)
    • Optional “chapter” splits per section for seekability.

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4. Which models make sense? (easier/cheaper/reliable)

Option A – Easiest & very reliable: hosted frontier “mini” model

Use a GPT-4.1 mini / GPT-5 mini or Claude 3.5 Sonnet/Haiku via API:

• Pros:

  • Super easy: one HTTP client, good docs, SDKs. citeturn0search1turn0search2turn0search8turn0search14
  • Excellent instruction following + style control.
  • Great English writing quality for podcast scripts.
  • Cheaper “mini” tiers usually cost a fraction of the flagship models.

• Cons:

  • Ongoing API cost (but for one script per recipe, usually cents or less per episode).
  • You rely on a vendor; data governance depends on their policies.

For a solo dev / small team just testing product-market fit, this is almost always the best choice.

My pick here:

• One of the OpenAI mini models (4.1 mini / 5 mini) or Claude Haiku/Sonnet for the script generation step.

Option B – Slightly harder, cheaper at scale: managed open-source

Use something like Llama 3.1 8B or 70B Instruct on a hosted platform (AWS, GCP, Azure, paid inference provider).

• Pros:
  • Per-request cost can be very low, especially for the 8B class.
  • You can fine-tune on your own recipe → podcast examples later for even more consistency. citeturn0search3turn0search15turn0search10
• Cons:
  • You must pick a provider, deal with model versions, etc.
  • Slightly more effort to match the polish of frontier closed models in style and safety.

This starts to shine if you’re generating a lot of episodes (thousands per day) or need more control/compliance.

Option C – Self-host open-source on your own GPU(s)

• Pros:
  • Max control over data; no third-party LLM vendor.
  • Potentially cheapest at very large scale.
• Cons:
  • You have to handle:
    • GPU infra + scaling
    • Model updates
    • Monitoring, logging, etc.
  • Overkill unless this becomes a big platform.

I’d only recommend this if you already have infra skills or this grows into a large commercial system.

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5. Practical recommendation for you

Given your goal and the fact you’re already planning to integrate ElevenLabs:

1. Start with a frontier “mini” model via API (OpenAI or Anthropic).

   • It’s genuinely the easiest and will be cheap for script-length usage.
   • You get strong text quality and reliability without fine-tuning.

2. Use the two-step LLM pattern:

   • Recipe → structured JSON
   • JSON → scripted, neurodiversity-friendly podcast text

3. Add simple validation logic:

   • Ingredient coverage check in code.
   • Optional LLM “checker” pass.

4. Once things work and you know your volumes/costs:

   • If cost is fine → stay with frontier mini models.
   • If cost becomes a problem → move the same prompts to a hosted Llama 3.1 8B/70B and compare.

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If you’d like, next step I can:

• Draft concrete example prompts for:
  • JSON extraction
  • Script generation, tuned for neurodiverse listeners
• And show how to shape the text so ElevenLabs can add nice pacing and sectioning automatically.

You

Is it possible to infer these models through huggingface? Claude Sonnet for example

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Short answer:
No, you can’t run Claude 3.5 Sonnet (or other Claude models) directly via Hugging Face.
You access Claude only via Anthropic’s API (or via partners that proxy it). Hugging Face is mostly for open-source models.

Let me break it down:

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1. Claude & Hugging Face

• Claude (including 3.5 Sonnet, Opus, Haiku) is a closed, hosted model.
• Anthropic exposes it through their own API and a few integrations (e.g., some cloud providers / tools), but they do not publish the model weights.
• Hugging Face is mainly for open-weight models (Llama, Mistral, etc.) plus their own Inference API. Since Claude weights aren’t public, you won’t see “Claude 3.5 Sonnet” as a HF model you can run locally or through HF Inference Endpoint.

You can, however:

• Use similar-quality open models on Hugging Face (Llama 3.x, Mistral, etc.).
• Or use HF’s “OpenAI-compatible” or other providers to hit external APIs from within some HF tools, but that’s still just calling Anthropic/OpenAI remotely, not “running Claude on Hugging Face.”

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2. What are your options for your project?

For your recipe → podcast-script pipeline:

Option A – Use Claude Sonnet via Anthropic API

If you specifically like Claude:

• Use Anthropic’s official SDKs / REST API.
• Your stack looks like:
  • Your backend → Anthropic API (Claude Sonnet) for:
    • recipe → structured JSON
    • JSON → neurodiverse-friendly script
  • Your backend → ElevenLabs API for TTS.

No Hugging Face needed at all.

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Option B – Use a HF-hosted open model instead of Claude

If you want to stay in the Hugging Face ecosystem:

• Pick a strong instruct model from HF (e.g. Llama 3.1 Instruct, Mistral-style models) via:
  • transformers + local GPU
  • or HF Inference Endpoints / text-generation-inference.
• You can then reproduce very similar behavior to Claude for this use case with good prompting (and maybe later fine-tuning).

For your “script generation for cooking podcasts,” these open models are more than good enough, especially if you use the two-step JSON → script pattern we discussed.

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3. Bottom line

• Direct Claude Sonnet inference through Hugging Face: ❌ Not possible (no public weights, no HF model card that you can just from_pretrained and run).
• Using Claude for your project: ✅ Yes, but via Anthropic’s API, not via Hugging Face.
• Using Hugging Face: ✅ Yes, with alternative open models (Llama, Mistral, etc.) that you host or run via HF services.

If you tell me which stack you’re most comfortable with (Python backend? Node? Any cloud preference?), I can sketch a concrete minimal example:

• one route using Claude API, and
• one route using a Hugging Face open model,

both wired up to ElevenLabs for the final audio.

You

And I assume I can’t run elevenLabs through HF either, right?

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Right, you can’t natively run ElevenLabs through Hugging Face in the same way you’d run a model hosted on HF (like transformers + HF Inference API).

But there are a couple of nuances:

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1. No native ElevenLabs model on HF

• ElevenLabs is a closed, hosted TTS service with its own API.
• They don’t publish their model weights on Hugging Face, so you can’t do:

  from transformers import AutoModel
  AutoModel.from_pretrained("elevenlabs/whatever")

  or host it as a Hugging Face Inference Endpoint.

So in that strict sense: no, you can’t “run ElevenLabs on HF”.

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2. How they can coexist

You can, though, use them side by side in your stack:

• Use Hugging Face for your LLM (e.g. Llama 3) or other NLP stuff.
• Use ElevenLabs API for TTS.

Typical Python flow:

1. Get / parse recipe.
2. Call HF-hosted LLM (or local transformers model) → generate podcast script.
3. Send script text → ElevenLabs TTS API → get audio file.
4. Save / stream that audio in your app.

They don’t conflict; they just live as two separate APIs you call from your backend.

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3. If you did want everything under one roof

If you really wanted only Hugging Face:

• You’d use an open-source TTS model from HF (e.g. Coqui TTS, Bark, xtts, etc.) and host it via:
  • HF Inference Endpoints, or
  • your own infra with transformers / tts libraries.

But for quality + ease, ElevenLabs is totally fine to keep as a separate service and pair with either:

• a frontier LLM (OpenAI / Anthropic) or
• a HF-hosted open model.

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If you want, next step I can sketch a tiny end-to-end example like:

• recipe_text -> LLM prompt -> script -> ElevenLabs API call (pseudo-code in Python),
  so you see how all the pieces glue together.

You

what about Comfi UI? can that accomodate Claude Sonnet and elevenlabs ?

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Short answer: ComfyUI isn’t a natural fit for Claude Sonnet or ElevenLabs. You can hack things together with custom nodes and HTTP calls, but it’s not what ComfyUI is designed for.

Let’s unpack it in practical terms 👇

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1. What ComfyUI is really for

ComfyUI is a node-based workflow UI for diffusion models (mainly Stable Diffusion and image generation / image editing pipelines).

It’s amazing for:

• Chaining image models (SDXL, ControlNet, Loras, etc.)
• Doing complex visual workflows: img2img, inpainting, stylization
• Visual experimentation with model graphs

It is not primarily built as an LLM + TTS orchestration layer. So:

• No native “Claude Sonnet” node
• No native “ElevenLabs” node
• Everything text/audio/LLM-related would have to be bolted on via custom nodes that call external APIs.

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2. Could it technically “accommodate” Claude + ElevenLabs?

In theory, yes, but only via custom API nodes:

You could:

1. Create a custom node that:

   • Takes recipe text as input
   • Calls the Anthropic API for Claude Sonnet
   • Returns the generated script text

2. Create another custom node that:

   • Takes the script text
   • Calls the ElevenLabs TTS API
   • Returns an audio file (or path to it)

Then your ComfyUI graph might look like:

Recipe Text Node → Claude Node → Script Node → ElevenLabs Node → Audio Output

But:

• You’d be writing and maintaining custom Python nodes.
• You’re fighting the tool a bit, because ComfyUI is visually centered on image tensors, not text/audio.
• You don’t really get any big advantage over just… writing a small Python backend.

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3. What I’d recommend instead for your project

Your project is:

Recipe → neurodiversity-friendly podcast script → ElevenLabs TTS

This fits much better into a simple backend service than a node-based diffusion UI.

A more natural stack

• Backend in Python or Node.js
• Call:
  • Claude Sonnet (Anthropic API) or another LLM (OpenAI, HF, etc.)
  • ElevenLabs API for TTS
• Optionally a tiny frontend for:
  • Pasting a recipe URL/text
  • Showing the generated script
  • Playing / downloading the audio

This will be:

• Far easier to reason about
• Easier to deploy (no GUI/workflow graphs)
• Easier to integrate with your app / website / podcast pipeline

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4. When ComfyUI would make sense for you

ComfyUI starts making sense if you later want:

• Visual assets for your recipes/podcast:
  • Episode cover art
  • Step-by-step images for each recipe
  • Social media graphics
• Then you could use ComfyUI for image workflows, and your LLM/TTS stack stays separate.

So the architecture could be:

• LLM + ElevenLabs: in code (no ComfyUI)
• Image generation for branding / episode art: in ComfyUI

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5. TL;DR

• Claude Sonnet in ComfyUI: only via custom HTTP/API nodes. No native support.
• ElevenLabs in ComfyUI: same story – only via custom nodes hitting their API.
• For your “audio recipe podcast for neurodiverse users” idea, a small backend that calls Claude + ElevenLabs directly is the clean, sane, maintainable option.

If you want, I can next:

• Sketch a minimal Python “pipeline script” that:
  1. Takes a recipe string
  2. Sends it to an LLM to structure + generate script
  3. Sends that script to ElevenLabs
  4. Saves an .mp3

so you have a concrete starting point you can adapt.

You

realistically can you list the complications for custom nodes in ComfyUI that hit the APIs I need? And can you give me the best way forward? In general I am familiar with python workflows. I have used HF in the past but I don’t think calling specific APIs will be too much faff for me.

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Realistically: yes, you can do Claude + ElevenLabs via custom ComfyUI nodes… but with your Python background, it’s almost certainly more hassle than it’s worth unless you really want the node-graph UI.

I’ll do two things:

1. List the concrete complications of custom API nodes in ComfyUI
2. Suggest a “best way forward” given your skills and goals

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1. Complications of custom ComfyUI nodes that call Claude / ElevenLabs

a) Dependency & environment juggling

To call Claude + ElevenLabs you’ll need extra Python deps (requests / SDKs etc.):

• ComfyUI has its own environment; you need to:
  • install dependencies without breaking the rest of the setup
  • keep versions compatible (e.g. if you install httpx, pydantic, etc.)
• Updating ComfyUI or other custom nodes can silently conflict with your deps.

Effect: minor at first, annoying over time.

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b) Auth & secrets

You’ll need to handle:

• API keys for Anthropic + ElevenLabs
• Not committing them into your node code
• Some way to set them:
  • environment variables
  • ComfyUI config file
  • custom “settings” node

ComfyUI has no built-in secret manager, so you’re hand-rolling this.

Effect: doable, just a bit ad-hoc.

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c) Blocking HTTP calls in a UI-driven graph

ComfyUI graphs run node-by-node in a pipeline:

• If your node does a network call that takes a few seconds, that node blocks the graph.
• If the call hangs or times out, your graph feels like it “freezes”.
• Streaming outputs (like streaming LLM tokens) are awkward; you’ll likely just do “fire request, wait for full text/audio, return”.

Effect: okay for small workloads, clunky for heavy use.

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d) Data types don’t perfectly match

ComfyUI is optimized for:

• images, latent tensors, masks, etc.

You want:

• long text (prompt/script)
• audio blobs/files

So you’ll be:

• passing long text via string sockets (fine but ugly)
• returning audio as:
  • file paths, or
  • raw bytes serialized somehow (usually file is simpler)

Effect: lots of “glue” logic that would be trivial in plain Python.

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e) Error handling & retries

With raw APIs you must handle:

• network errors
• Anthropic / ElevenLabs rate limits
• malformed responses
• user cancelling a workflow mid-run

In ComfyUI, if your node throws, you basically get:

• red error
• maybe an exception traceback in logs

You don’t have a nice place to:

• do incremental backoff
• retry strategies
• structured error messages back to the user

Effect: debugging and robustness are worse than a small backend service.

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f) Versioning & maintainability

If you build this in ComfyUI:

• Logic is spread across:
  • Python node definitions
  • UI wiring in the graph
• Changing the pipeline (e.g. adding a JSON-validation step, extra LLM pass) means:
  • editing nodes and rewiring the graph
  • migrating saved workflows when nodes change signature

In plain Python:

• It’s just… functions and modules. Git-friendly, testable, refactorable.

Effect: harder to keep clean as the project grows.

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g) Production deployment & scaling

If this ever gets serious:

• Do you really want a ComfyUI server as your production core?
• Scaling horizontally means:
  • multiple ComfyUI instances, shared storage, load balancing…
• CI/testing of workflows is less straightforward than testing Python functions.

Effect: Comfy is great for interactive creative tinkering, not for “core production service that glues LLM + TTS”.

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2. Best way forward (given your skills)

You’re comfortable with Python and APIs; you’ve used HF before. That’s perfect. I’d suggest:

Step 1 – Build a small Python pipeline (no ComfyUI)

Structure it like:

recipe_text
  → llm_normalize(recipe_text)        # recipe → structured JSON
  → validate_recipe_json(json)        # sanity checks
  → llm_script_from_json(json)        # JSON → podcast script
  → tts_generate(script_text)         # ElevenLabs → audio
  → save/play audio file

Concretely:

• llm_normalize & llm_script_from_json call:
  • Claude Sonnet via Anthropic API or
  • Another LLM if you change provider
• tts_generate calls ElevenLabs

All just regular Python functions/modules, e.g.:

def llm_script_from_json(recipe_json: dict) -> str:
    prompt = build_prompt(recipe_json)  # your neurodiversity-aware instructions
    # call Claude / OpenAI here, return script_text
    ...

This gives you:

• Simple logging & error handling
• Easy retries
• Proper unit tests (fake LLM/TTS responses)
• Clear separation of concerns

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Step 2 – Optionally expose it as a simple API

Use something like FastAPI or Flask:

• POST /generate-episode with the recipe (or URL)
• It returns:
  • episode metadata
  • link/path to the generated audio file
  • the script (for debugging or transcript)

Then you can:

• build a small web UI later
• or call it from other tools (even from ComfyUI if you really want)

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Step 3 – If you still want ComfyUI in the loop

Instead of putting Claude + ElevenLabs logic inside Comfy:

• Keep all your real logic in the Python backend.
• Write one tiny custom ComfyUI node that just calls your backend’s /generate-episode API.

So ComfyUI becomes:

• a thin “client” node that says:

  “Hey backend, here’s a recipe; give me a ready-made audio file + maybe the cover image URL”

This way:

• All the complexity (auth, retries, validation, Claude prompts, ElevenLabs quirks) lives in clean Python code.
• The Comfy node is tiny and stable.
• You can swap or upgrade LLM/TTS providers in backend code without touching Comfy graphs.

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3. TL;DR recommendation

Given everything you said:

• Don’t implement the core logic as ComfyUI nodes. The complications (deps, data types, error handling, production-ization) are needless friction.
• Do build a small, clean Python pipeline that:
  • calls Claude (or another LLM) for recipe → JSON + JSON → script
  • calls ElevenLabs for TTS
• If you ever want a visual graph for some reason:
  • have ComfyUI call your backend as a black-box “episode generator” microservice.

If you’d like, I can next sketch:

• A minimal Python module layout (llm_client.py, tts_client.py, pipeline.py), and
• One example of the prompts for the JSON + script steps, tuned explicitly for neurodiverse listeners.

You

I was wondering why you suggested Claude 3.5 Sonnet and not 4.5

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Good catch — and fair question 🙂

Why I said Claude 3.5 Sonnet instead of Sonnet 4.5

There are two parts to this:

1. What 4.5 is optimised for
2. What your project actually needs

1. What Sonnet 4.5 is about

Claude Sonnet 4.5 is Anthropic’s newer, more capable “Sonnet” model. It’s marketed as:

• “Best model for complex agents and coding”
• Great at long-horizon, tool-using, computer-use tasks, and massive codebases citeturn0search5turn0search9turn0news23
• Same ballpark pricing as the earlier Sonnet mid-tier models ($3 / $15 per million tokens). citeturn0search3turn0search5turn0search15

In other words: it’s pushed as an agent/coding monster, able to autonomously work for many hours, manage tools, refactor big repos, etc.

2. Why I still reached for 3.5 Sonnet in the conversation

For your use case — recipe → structured JSON → friendly podcast script for neurodiverse listeners — you mainly need:

• Strong instruction following
• Stable formatting and structure (sections, steps, ingredients)
• Good, controlled, empathetic writing

You do not need:

• Long-horizon autonomous agents
• Computer-use / tool orchestration
• Extreme coding performance

Claude 3.5 Sonnet is already very strong at general reasoning + writing and is widely integrated (Bedrock, Vertex, etc.), and the cost profile is the same mid-tier level. citeturn0search0turn0search2turn0search8turn0search10

So my mental shortcut was:

“Mid-tier, widely available Claude model that’s good at instructions + writing and cheap enough” → 3.5 Sonnet is perfectly fine.

For your pipeline, you’ll barely notice a practical difference between 3.5 Sonnet and 4.5 Sonnet in simple recipe scripting—most of 4.5’s extra power is in areas you’re not using.

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What should you actually pick?

If you’re starting fresh and have access to all of them, a sensible rule of thumb:

• Default: use the latest Sonnet mid-tier available (today that’s Sonnet 4.5).
• Optimize later: if you ever find the cost/latency overkill for your fairly simple prompts, you can:
  • Drop down to Haiku 4.5 / 3.5 for cheaper runs, or
  • Mix: Haiku for JSON extraction, Sonnet for the final “nice” script.

So practically, if you’re wiring this up now and you’re comfortable with APIs:

Use claude-sonnet-4-5 as your main model.
Keep your prompts + pipeline modular so you can swap to 3.5 Sonnet or Haiku if you want to experiment with costs.

If you’d like, I can rewrite the earlier pipeline sketch but with concrete “use claude-sonnet-4-5 here, haiku-4.5 here” style recommendations and example Python calls.

You

trying to implement claude but it’s not working

import anthropic #from dotenv import load_dotenv import os

#load_dotenv() CLAUDE_KEY = os.getenv(“ANTHROPIC_API_KEY”)

client = anthropic.Anthropic(api_key=CLAUDE_KEY)

message = client.messages.create( model=“claude-sonnet-4-5”, max_tokens=1000, messages=[ { “role”: “user”, “content”: “What are the key elements that would make a cooking recipe more accessible to people with ADHD?” } ] ) print(message.content)

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You’re super close 🙂 There are a few very common “gotchas” in that snippet. I’ll walk through the likely ones and give you a known-good example.

---

1. Your .env isn’t being loaded

You have:

#from dotenv import load_dotenv
import os

#load_dotenv()
CLAUDE_KEY = os.getenv("ANTHROPIC_API_KEY")

Both the import and load_dotenv() are commented out, so unless you’ve exported ANTHROPIC_API_KEY in your shell, CLAUDE_KEY will be None.

That will make the SDK construct a client with no key and all calls will fail.

Fix:

Either:

Option A – actually load .env

from dotenv import load_dotenv
import os
import anthropic

load_dotenv()  # <— important

CLAUDE_KEY = os.getenv("ANTHROPIC_API_KEY")
client = anthropic.Anthropic(api_key=CLAUDE_KEY)

Or:

Option B – skip dotenv and use os.environ[...]

Set your key in environment (e.g. bash):

export ANTHROPIC_API_KEY="sk-ant-..."

Then in Python:

import os
import anthropic

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

(Using os.environ[...] will throw a clear error if the key is missing, which is nice.)

---

2. Make sure the SDK & model name are correct

Your code structure is right for the current Anthropic Python SDK. Just make sure you’re on the modern one:

pip install --upgrade anthropic

Model name "claude-sonnet-4-5" is valid – Anthropic exposes it with API alias claude-sonnet-4-5. citeturn0search0

---

3. Accessing the response correctly

message.content is a list of content blocks, each with type and text. Printing the whole thing will show an object repr, but it should still work.

To get the text cleanly:

for block in message.content:
    if block.type == "text":
        print(block.text)

Or if you know it’s just one text block:

print(message.content[0].text)

---

4. Full minimal working example

Put this in e.g. test_claude.py:

import os
from dotenv import load_dotenv
import anthropic

load_dotenv()  # make sure .env is loaded

client = anthropic.Anthropic(
    api_key=os.environ["ANTHROPIC_API_KEY"],  # will raise if missing
)

message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=500,
    messages=[
        {
            "role": "user",
            "content": "What are the key elements that would make a cooking recipe more accessible to people with ADHD?"
        }
    ],
)

for block in message.content:
    if block.type == "text":
        print(block.text)

Then run:

ANTHROPIC_API_KEY="sk-ant-..." python test_claude.py
# or put the key in .env and just run: python test_claude.py

---

5. Quick sanity checks if it still “doesn’t work”

In order, I’d add these lines and confirm:

print("Key present?", CLAUDE_KEY is not None)
print("Key starts with:", CLAUDE_KEY[:8] if CLAUDE_KEY else None)
print("anthropic version:", anthropic.__version__)

If Key present? prints False → your env var / dotenv is the issue.
If there’s a stack trace, the message at the bottom will usually be either:

• Auth problem (bad/missing key), or
• Model not available to your account / wrong ID.

If you paste that error message into your head, you’ll almost always see which of the above it is.

If you want, I can also sketch a small wrapper function like call_claude(prompt: str) -> str that you just drop into your recipe→podcast project so you don’t have to think about the SDK details again.

You

ok i got it working, but the response comes like that [TextBlock(citations=None, text=’# Key Elements for ADHD-Friendly Recipes\n\nVisual clarity:\n- Clear section breaks between ingredients and steps\n- Bullet points or numbered lists (not dense paragraphs)\n- Bold text for key actions or ingredients\n- Photos at critical steps, not just the final dish\n\nSimplified structure:\n- Total time estimate at the top (including prep, cook, and wait times)\n- Ingredient list in order of use\n- “Mise en place” checklist to gather everything first\n- Steps broken into small, single actions\n\nReduced cognitive load:\n- One cooking method per step (don’t say “chop onions while water boils”)\n- Specific measurements (not “season to taste” without guidance)\n- Timer reminders built into instructions\n- What the food should look/smell/sound like as checkpoints\n\nEngagement hooks:\n- Why this step matters (helps maintain focus)\n- Permission to take breaks at natural stopping points\n- Flexible substitutions clearly marked\n- Estimated active vs. passive time\n\nError prevention:\n- Common mistakes flagged in advance\n- “Did you remember to…?” prompts\n- Equipment list upfront\n- Temperature/setting confirmations\n\nThe goal is removing the need to hold multiple pieces of information in working memory while making the process feel manageable rather than overwhelming.’, type=‘text’)]

how can get the text and print as new line content when indicated in the response formatted with \n\n?

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Nice, congrats on getting it working 🎉

What you’re seeing is just the Python representation of the TextBlock object, not the actual printed text. You need to access the .text field inside each block.

1. Get the text from the response

# message is the result of client.messages.create(...)

for block in message.content:
    if block.type == "text":
        print(block.text)

That block.text is a normal Python string — the \n\n inside it will render as real new lines when you print it.

If you know there’s only one text block, you can just do:

text = message.content[0].text
print(text)

2. If you want one combined string

Sometimes Claude can return multiple text blocks. To be safe:

text = "".join(
    block.text
    for block in message.content
    if block.type == "text"
)

print(text)

That’s it — no special handling for \n\n needed. Those are already newline characters; they only looked “escaped” because you were seeing the object’s repr instead of the actual printed string.

You

is this the only way to query claude?

message = client.messages.create( model=“claude-sonnet-4-5”, max_tokens=1000, messages=[ { “role”: “user”, “content”: “What are the key elements that would make a cooking recipe more accessible to people with ADHD?” } ] )

was wondering how I can use the console to consistently prompt it and print the responses. Can you thoroughly explain this role and content tags? are there more options?

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You’re not stuck with just that one pattern, and yeah, we can absolutely make it feel like a little “Claude console” where you type prompts and get answers back.

I’ll break it down into:

1. How messages=[{"role": ..., "content": ...}] actually works
2. What other roles and content types exist
3. How to build a simple reusable ask_claude() function
4. How to make a tiny REPL-style console to chat with Claude

---

1. What messages=[{"role": ..., "content": ...}] actually is

Anthropic’s messages.create(...) takes a conversation history as a list of message objects.

Basic structure

message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1000,
    messages=[
        {
            "role": "user",
            "content": "Your prompt goes here"
        }
    ]
)

This means:

• model: which Claude model to use (claude-sonnet-4-5, claude-haiku-latest, etc.)
• max_tokens: how many tokens Claude is allowed to generate in the response
• messages: array of turns in the conversation

Each turn is:

{
  "role": "user" | "assistant" | "system",
  "content": ...
}

• "user": things you say
• "assistant": previous Claude replies (if you’re keeping chat history)
• "system": high-level instructions about how Claude should behave

Simple one-off request

For a single request/response, you only need one user message:

messages=[
    {"role": "user", "content": "Write a haiku about soup"}
]

That’s perfectly fine for your recipe → explanation / script use case.

---

2. Roles & content explained (and extra options)

Roles

Anthropic supports these main roles in messages:

1. "system" – “global instructions”

   • Sets behavior, style, constraints.
   • Example: “You are a helpful assistant that writes clear, concise cooking instructions for neurodiverse people.”
   • Goes at the start of the messages list.

2. "user" – end-user input

   • Everything you or your app’s user types.

3. "assistant" – model replies (if you’re continuing a conversation)

   • You copy the previous response text back in with role "assistant" when you want Claude to “remember” context.

Example with all three:

messages = [
    {
        "role": "system",
        "content": "You are an assistant that writes neurodiversity-friendly recipe explanations."
    },
    {
        "role": "user",
        "content": "Explain what mise en place means for someone with ADHD."
    },
    {
        "role": "assistant",
        "content": "Mise en place is a French term..."
    },
    {
        "role": "user",
        "content": "Thanks. Now give me a checklist I can print."
    }
]

New call with that list will let Claude continue the conversation properly.

---

Content types

In your simple example you used:

"content": "some string"

Under the hood, Anthropic treats that as a single text block. Content can also be a list of blocks, each with a type:

• "text" – normal text
• "image" – image input (if you ever do multimodal)
• there are other advanced block types, but for now text is all you need.

Explicit “blocks” form:

"content": [
    {"type": "text", "text": "What are key elements...?"}
]

For most cooking/script use you can just pass a string like you’re already doing.

---

3. A reusable ask_claude() helper

To avoid rewriting the same client.messages.create(...) all the time, make a small helper.

Minimal version (one-off, no history)

import anthropic
import os

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

def ask_claude(prompt: str, model: str = "claude-sonnet-4-5", max_tokens: int = 1000) -> str:
    message = client.messages.create(
        model=model,
        max_tokens=max_tokens,
        messages=[
            {"role": "user", "content": prompt}
        ],
    )
    # Combine text blocks (usually there's just one)
    return "".join(
        block.text for block in message.content if block.type == "text"
    )

# usage
answer = ask_claude("What are the key elements that would make a recipe ADHD-friendly?")
print(answer)

Now anywhere in your code you can just call ask_claude(...).

---

With a system prompt baked in

For your project you probably want a consistent style (neurodiversity-friendly, stepwise, clear). You can embed that in a system message:

SYSTEM_PROMPT = """
You are an expert cooking assistant who writes clear, step-by-step instructions
for neurodivergent people, especially those with ADHD and autism.

Your goals:
- Reduce cognitive load
- Use short sentences
- Make time/temperature explicit
- Avoid idioms and jokes that might confuse
"""

def ask_claude(prompt: str, model: str = "claude-sonnet-4-5", max_tokens: int = 1000) -> str:
    message = client.messages.create(
        model=model,
        max_tokens=max_tokens,
        messages=[
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ],
    )
    return "".join(
        block.text for block in message.content if block.type == "text"
    )

Now every call carries that “persona” automatically.

---

4. Making a simple interactive console (REPL)

You can make a tiny script that lets you chat with Claude in your terminal:

import os
import anthropic

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

SYSTEM_PROMPT = """
You are a helpful assistant specialized in making cooking recipes accessible
for neurodivergent people. Be clear, kind, and concrete.
"""

def chat():
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT}
    ]

    print("Chat with Claude (type 'exit' to quit)\n")
    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit"}:
            break

        messages.append({"role": "user", "content": user_input})

        resp = client.messages.create(
            model="claude-sonnet-4-5",
            max_tokens=800,
            messages=messages,
        )

        text = "".join(
            block.text for block in resp.content if block.type == "text"
        )

        print("\nClaude:\n" + text + "\n")

        # append Claude’s reply to history so it has context next turn
        messages.append({"role": "assistant", "content": text})

if __name__ == "__main__":
    chat()

What this does:

• Keeps a messages list in memory.
• Every time you type:
  • Adds a {"role": "user", "content": ...} entry.
  • Calls messages.create with the full conversation.
  • Prints Claude’s reply.
  • Adds Claude’s reply back as {"role": "assistant", ...} for context next time.

That’s basically your own minimal “Claude console”.

---

5. Summary of “are there more options?”

For your use case (recipes → scripts) the main knobs you’ll care about are:

• model – which Claude (Sonnet, Haiku, etc.)
• max_tokens – upper bound of reply length
• system vs user vs assistant roles in messages
• temperature, top_p, etc. for creativity vs. determinism if you want to tweak later
• Returning structured outputs (JSON) by carefully prompting

But structurally, yes: all queries go through client.messages.create(...) with some messages=[...], you just:

• add a system message for persona
• optionally maintain a conversation history
• wrap it in helpers so you don’t have to think about it all the time

If you tell me how you imagine your end-to-end flow (e.g. “I paste recipe → it outputs JSON → second call returns podcast script”), I can give you ready-made ask_for_json() and ask_for_script() functions using this pattern.

You

What I have in mind as the first step is a simplification/breakdown of the recipe. First I need to identify the ingredients. These have three distinct characteristics:

1. the ingredient name
2. the quantity needed
3. a preprocess step (for example chopped, boiled, etc)

So i need to be able to paste a recipe and first extract those. These will provide the base for the recipe. What’s the best format to get those at? Is it a json, is it something else? Justify your response.

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For what you’re trying to do, JSON is absolutely the right target format for the ingredients step.

Let me justify that and show you what I’d actually use.

---

Why JSON is the best fit here

You want to:

1. Paste messy human text (a recipe).
2. Extract structured data:
   • ingredient name
   • quantity
   • preprocess step (chopped, boiled, peeled, etc.)
3. Use that as the base for later steps (script generation, TTS, UI, checks, etc.).

For that job, JSON wins over “just text” or things like CSV because:

1. It’s made for structured, nested data

You naturally have an array of ingredients, and each ingredient has multiple named fields:

• name
• quantity
• preprocess
• potentially more later (unit, notes, group, etc.)

JSON:

[
  {
    "name": "onion",
    "quantity": "1",
    "preprocess": "finely chopped"
  }
]

is directly representing what you conceptually have. CSV or plain text would force you into awkward parsing again later.

---

2. It’s easy to consume from everything

JSON is:

• Native in Python (json.loads, json.dumps)
• Native in JS/TS (just objects/arrays)
• Supported by basically every language, DB, or API

So from your pipeline’s point of view:

• LLM → JSON
• Your code → manipulates that JSON
• Later stages (script generator, frontend UI, analytics) → can reuse the same structure

You don’t have to re-parse another custom text format every time.

---

3. It’s perfect for validation & safety checks

You will want to check things like:

• Does every ingredient have a name and quantity?
• Is quantity missing or non-sensical?
• Did the LLM hallucinate weird fields?

With JSON, you can:

• Write simple Python checks, or
• Use a schema (Pydantic, JSON Schema) to validate

Example Pydantic model:

from pydantic import BaseModel
from typing import Optional, List

class Ingredient(BaseModel):
    name: str
    quantity: str
    preprocess: Optional[str] = None
    original_text: Optional[str] = None

class IngredientList(BaseModel):
    ingredients: List[Ingredient]

That gives you strong guarantees before passing stuff down the pipeline.

---

4. It’s future-proof for your use case

Right now you care about:

1. name
2. quantity
3. preprocess

Later, you might want:

• unit (g, ml, tbsp, cups, etc.)
• group (e.g. “for the sauce”, “for the topping”)
• notes (“room temperature”, “optional”)
• allergen_tags, category (“protein”, “carb”), etc.

JSON makes it trivial to grow:

[
  {
    "name": "butter",
    "quantity": "100",
    "unit": "g",
    "preprocess": "softened",
    "notes": "room temperature"
  }
]

No format change needed; your code can slowly adopt those fields.

---

5. It plays very nicely with LLM prompting

LLMs are already very good at “spit out valid JSON like this schema”. You can:

• Ask Claude to output exactly:

{
  "ingredients": [
    {
      "name": "string",
      "quantity": "string",
      "preprocess": "string or null",
      "original_text": "original ingredient line"
    }
  ]
}

• Then json.loads() and you’re done.

Natural language output (like bullet lists) would look nice for humans but force you to parse it again. JSON flips it: human readability is okay, but machine readability is excellent.

---

A concrete JSON shape I’d recommend

For your needs now but flexible for later:

{
  "ingredients": [
    {
      "name": "yellow onion",
      "quantity": "1",
      "preprocess": "finely chopped",
      "original_text": "1 yellow onion, finely chopped"
    },
    {
      "name": "olive oil",
      "quantity": "2 tbsp",
      "preprocess": null,
      "original_text": "2 tablespoons olive oil"
    }
  ]
}

Field reasoning:

• name: normalized ingredient name (“yellow onion” vs “1 onion”)
• quantity: whatever string is needed (“2 tbsp”, “1 cup”, “a pinch”)
• preprocess: just the action part (“finely chopped”, “boiled”, “peeled and sliced”); null if none
• original_text: the raw line from the recipe, so you can refer back or debug

You can always split quantity later into amount + unit if you want to normalize, but early on, a single string is less brittle.

---

How this plugs into your pipeline

Step 1 (what you’re planning):

Paste full recipe text → Claude → get ingredients JSON only.

Typical call:

• System message: describe the JSON schema and rules.
• User message: full recipe text.
• Output: a single JSON object exactly as above.

Then your code:

• json.loads(response_text)
• Validate / check ingredients
• Use that as the base for all later steps (script, variations, shopping lists, etc.)

---

So: yes – JSON is the best target format here: it’s structured, easy to parse, easy to validate, and future-proof for your recipe → script → TTS pipeline.

If you want, next we can write an actual Claude prompt that reliably turns a pasted recipe into exactly this JSON, and a small Python function that wraps it.

You

yeah I’d like that

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Nice, let’s turn this into something you can actually drop into your code.

I’ll give you:

1. A system prompt you can reuse
2. A user prompt template for “here’s a recipe → give me ingredients JSON”
3. A Python helper function extract_ingredients(recipe_text: str) -> dict using the Anthropic client

---

1. System prompt for ingredient extraction

This sets the behavior and JSON schema:

INGREDIENT_SYSTEM_PROMPT = """
You are an expert cooking assistant that extracts ingredients from recipes
into clean, machine-readable JSON.

Your task:
- Read the recipe text.
- Identify every ingredient.
- For each ingredient, output:
  - name: core ingredient name only (no quantity, no preprocess)
  - quantity: the quantity string exactly as written (e.g. "2 tbsp", "1 cup", "a pinch")
  - preprocess: any preparation step (e.g. "finely chopped", "boiled", "peeled and sliced"),
    or null if there is no explicit preparation.
  - original_text: the original ingredient line from the recipe.

Rules:
- Do NOT include equipment (pans, bowls, ovens) as ingredients.
- Do NOT include times or temperatures as ingredients.
- If the recipe has sections (e.g. "For the sauce"), still put all ingredients
  together in a single list; you may add the section name in a "group" field if present.
- If you are unsure about preprocess, set it to null rather than guessing.

Output format:
- Respond with a SINGLE valid JSON object.
- No markdown, no explanations, no extra text.
- The JSON MUST have the shape:

{
  "ingredients": [
    {
      "name": "string",
      "quantity": "string",
      "preprocess": "string or null",
      "original_text": "string",
      "group": "string or null"
    }
  ]
}

If there is no group, set "group" to null.
"""

Note: I added group as optional – it’s handy later, and you can just ignore it if you don’t need it yet.

---

2. User prompt template

This is what you send as "role": "user":

def build_ingredient_user_prompt(recipe_text: str) -> str:
    return f"""
Extract the ingredients from the following recipe text into the JSON format described.

RECIPE TEXT:
\"\"\"{recipe_text}\"\"\"
"""

Nothing fancy: just “here’s the recipe, follow the system instructions.”

---

3. Python helper function using Claude

Here’s a minimal but solid helper you can drop into your project.

import os
import json
import anthropic

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

INGREDIENT_SYSTEM_PROMPT = """
You are an expert cooking assistant that extracts ingredients from recipes
into clean, machine-readable JSON.

Your task:
- Read the recipe text.
- Identify every ingredient.
- For each ingredient, output:
  - name: core ingredient name only (no quantity, no preprocess)
  - quantity: the quantity string exactly as written (e.g. "2 tbsp", "1 cup", "a pinch")
  - preprocess: any preparation step (e.g. "finely chopped", "boiled", "peeled and sliced"),
    or null if there is no explicit preparation.
  - original_text: the original ingredient line from the recipe.
  - group: section/group name if the recipe has sections like
    "For the sauce" or "For the topping"; otherwise null.

Rules:
- Do NOT include equipment (pans, bowls, ovens) as ingredients.
- Do NOT include times or temperatures as ingredients.
- If the recipe has sections (e.g. "For the sauce"), still put all ingredients
  together in a single list; you may use "group" to indicate the section.
- If you are unsure about preprocess, set it to null rather than guessing.

Output format:
- Respond with a SINGLE valid JSON object.
- No markdown, no explanations, no extra text.
- The JSON MUST have the shape:

{
  "ingredients": [
    {
      "name": "string",
      "quantity": "string",
      "preprocess": "string or null",
      "original_text": "string",
      "group": "string or null"
    }
  ]
}

If there is no group, set "group" to null.
"""

def build_ingredient_user_prompt(recipe_text: str) -> str:
    return f"""
Extract the ingredients from the following recipe text into the JSON format described.

RECIPE TEXT:
\"\"\"{recipe_text}\"\"\"
"""

def extract_ingredients(recipe_text: str, model: str = "claude-sonnet-4-5") -> dict:
    """
    Calls Claude to extract ingredients from a recipe.

    Returns a Python dict with shape:
    {
      "ingredients": [
        {
          "name": ...,
          "quantity": ...,
          "preprocess": ...,
          "original_text": ...,
          "group": ...
        },
        ...
      ]
    }
    """
    user_prompt = build_ingredient_user_prompt(recipe_text)

    response = client.messages.create(
        model=model,
        max_tokens=1000,
        messages=[
            {"role": "system", "content": INGREDIENT_SYSTEM_PROMPT},
            {"role": "user", "content": user_prompt},
        ],
    )

    # Concatenate all text blocks into one string
    raw_text = "".join(
        block.text for block in response.content if block.type == "text"
    )

    # Try to parse JSON
    try:
        data = json.loads(raw_text)
    except json.JSONDecodeError as e:
        # Optional: print debug info or raise a clearer error
        raise ValueError(f"Claude did not return valid JSON: {e}\nRaw output:\n{raw_text}")

    # Optional: quick sanity check
    if "ingredients" not in data or not isinstance(data["ingredients"], list):
        raise ValueError(f"JSON does not have expected 'ingredients' list: {data}")

    return data

Usage example:

if __name__ == "__main__":
    recipe_text = """
    For the sauce:
    - 2 tbsp olive oil
    - 1 small onion, finely chopped
    - 2 cloves garlic, minced
    - 400g canned tomatoes

    For serving:
    - 200g spaghetti
    - Grated parmesan cheese, to taste
    """

    result = extract_ingredients(recipe_text)
    for ing in result["ingredients"]:
        print(ing)

You’ll get rows like:

{
  'name': 'olive oil',
  'quantity': '2 tbsp',
  'preprocess': None,
  'original_text': '- 2 tbsp olive oil',
  'group': 'For the sauce'
}

---

If you want, next step we can do Step 2 of your pipeline: given this JSON, design a second Claude prompt that turns it into a neurodiversity-friendly step-by-step script ready to feed into ElevenLabs.

You

interesting how the json format goes in the system propmt. Can you expand on that?

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Yeah, that “JSON spec in the system prompt” isn’t just vibes – it’s doing a lot of work for you. Let’s unpack what’s going on and how to use it well.

I’ll break it into:

1. Why we put the JSON format in the system prompt
2. How the model actually “uses” that schema
3. Patterns to make JSON output more reliable
4. Variations you can try as your project grows

---

1. Why the JSON spec lives in the system prompt

You could technically put the JSON instructions in any message, but the system role has a special meaning:

• system: “global rules / persona / contract – always obey this”
• user: “task + content – what I want this time”

By putting the JSON structure in the system prompt, you’re telling Claude:

“No matter what recipe I send later, you always:

• behave like this kind of assistant
• follow this schema
• obey these rules about format.”

Then each user message can just say “Here’s a new recipe, extract ingredients”, and the system message still applies.

Why that’s nice for you:

• You only define the schema once, in one place.
• You can reuse the same system prompt for many recipes.
• You can change the schema in one file and all calls respect it.

So conceptually:

• System = “contract: output JSON with these fields”
• User = “input: here’s this specific recipe text”

---

2. How the model actually “uses” the schema

Large language models are basically autocomplete beasts: they try to produce the next token that best fits all their conditioning:

1. System message (highest priority)
2. Previous messages (conversation history)
3. Latest user message

When you paste something like:

Output format:
- Respond with a SINGLE valid JSON object.
- No markdown, no explanations, no extra text.
- The JSON MUST have the shape:

{
  "ingredients": [
    {
      "name": "string",
      "quantity": "string",
      "preprocess": "string or null",
      "original_text": "string",
      "group": "string or null"
    }
  ]
}

…you are basically giving it a template of what “looks right” as the next tokens. The model learns:

• There should be a top-level key "ingredients".
• That should be a list.
• Each entry should be an object with those keys.
• Values should be strings or null.

So when it starts generating, it’s “pulled” toward something that looks very close to that example JSON.

That’s why being explicit and concrete (like we did) helps a lot more than just saying “please output JSON”.

---

3. Patterns to make JSON output more reliable

a) Be very explicit in the system prompt

What we’re already doing is basically the “golden pattern”:

• Describe the task
• Describe each field and its meaning
• Show exact sample JSON with all keys
• Say “no markdown, no explanations, only JSON”

This gives the model:

• Semantics (“what preprocess is”)
• Syntax (“how an object with that key looks”)

b) Don’t mix multiple formats in the same call

If the system prompt says “output JSON only” but the user message says:

“Explain your reasoning and then output JSON”

…you’ve given it conflicting signals, and it might prepend text before the JSON. So keep each call single-purpose:

• This call = structure-only (JSON)
• Later call = talky, natural language output

That’s why your pipeline idea is good:
Call 1: “JSON ingredients only”
Call 2: “Now use that JSON to write a nice script”.

c) Use strong language about not adding extras

Small wording details matter:

• ✅ “Respond with a SINGLE valid JSON object. No markdown, no explanations, no extra text.”
• ❌ “Respond with JSON, and feel free to add comments if needed.”

The second one invites non-JSON output.

d) Add example values for types like "string or null"

If you want null allowed, you can even show an example:

{
  "name": "olive oil",
  "quantity": "2 tbsp",
  "preprocess": null,
  "original_text": "- 2 tbsp olive oil",
  "group": "For the sauce"
}

The model then knows “oh, null is literal null, not a string ‘null’”.

e) Validate + retry on your side

Even with good prompting, sometimes you’ll get:

• A trailing comment
• Slightly malformed JSON
• Or a correct shape but wrong type in a field

So your Python should:

1. json.loads()
2. If it fails or the shape is wrong:
   • either raise (for debugging), or
   • send that output back to Claude and say “Fix this to valid JSON following the schema.”

Because LLMs are great at fixing their own output when you show them what broke.

---

4. Variations / future upgrades

Once you’re comfy with the “schema in system prompt” pattern, there are a few nice variations:

a) Multiple schemas for different steps

You can have:

• INGREDIENT_SYSTEM_PROMPT – for ingredients JSON
• STEPS_SYSTEM_PROMPT – for normalized steps JSON
• SCRIPT_SYSTEM_PROMPT – for final podcast script (maybe also JSON with sections)

Each one is a different behaviour profile with its own output contract.

b) Two-stage JSON

Sometimes it’s helpful to have:

1. ingredients array
2. metadata object

Like:

{
  "title": "Spaghetti with Tomato Sauce",
  "servings": 4,
  "ingredients": [ ... ]
}

Same idea: describe this in system prompt, show an example.

c) “Explain-then-JSON” with two calls

If you ever want extra reliability:

• Call 1: Ask Claude to think and write in natural language how it will parse each line.
• Call 2: Give it its own analysis and ask for pure JSON.

That often helps when recipes are weird or ambiguous, but you probably don’t need it until you hit edge cases.

---

Putting it in words:

The reason it’s “interesting” to put JSON format in the system prompt is that you’re essentially giving Claude a mini spec / contract for its output. The system role makes that spec persistent and high-priority, and the explicit JSON example gives the model a concrete shape to imitate. Combined, that’s what makes structured ingredient extraction actually usable in code instead of just “a nice text answer”.

If you’d like, next I can show you the next step JSON schema: how to represent the normalized cooking steps (with clear actions, times, references to ingredients) as JSON, so your later “podcast script generator” has a really solid base to work from.

You

I guess the reason i asked is because system feels like a more general direction and i wanted to understand if we can change that system prompt later without breaking/conflicting the LLM

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Totally fair question, and you’re thinking about it in exactly the right way.

Short answer:

• The system prompt is not permanent or magical.
• You can change it per call, or between stages of your pipeline.
• You just need to avoid conflicting instructions inside a single call.

Let’s unpack how to do this safely.

---

1. System prompt = “rules for this call / conversation”

If you’re using the API like this:

response = client.messages.create(
    model="claude-sonnet-4-5",
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": USER_PROMPT},
    ],
)

Then:

• That SYSTEM_PROMPT applies only to this API call.
• Next call, you can send a completely different system prompt and Claude won’t “remember” the old one unless you include it again.

So for your multi-step pipeline you can absolutely do:

1. Call 1: “You are an ingredient extractor, output JSON like this…”
2. Call 2: “You are a script writer for neurodivergent listeners, output a podcast script like this…”
3. Call 3: “You are a QA checker, compare recipe JSON and script, output issues…”

Each step has its own system prompt tailored to the task. That’s actually the ideal pattern.

---

2. When do system prompts conflict?

Conflicts only happen when you put incompatible instructions in the same conversation context. For example:

messages = [
    {"role": "system", "content": "Only ever respond in strict JSON."},
    {"role": "system", "content": "Explain your reasoning in plain English."},
    {"role": "user", "content": "Extract ingredients from this recipe."},
]

Now the model is being told to:

• Only respond in JSON
• …and also explain in plain English

You’ll often get mixed output: explanation + JSON, or JSON inside markdown, etc.

So the rule of thumb:

• Per API call, keep one coherent “output contract” in system messages.
• Don’t say “JSON only” and “also chatty explanation” at the same time.

In your pipeline, this is easy:

• Extraction call: system says “JSON only, format X”.
• Script call: different system says “natural language, structure Y”.

They never see each other, so there’s no conflict.

---

3. How to safely change the system prompt over time

Let’s say 2 months from now you want to tweak your ingredient schema (e.g. add unit and amount), and you’re worried it “breaks” Claude.

Good news: it doesn’t break the model; it just changes the spec you’re giving it.

You’d do:

1. Update the system prompt schema example:

   {
     "name": "onion",
     "quantity": "1",
     "unit": "piece",
     "preprocess": "finely chopped",
     ...
   }

2. Update your Python validation / data model.
3. Re-run some test recipes to ensure it behaves.

Claude doesn’t cache the old schema. It just sees the current system prompt and generates accordingly.

The only “breaking change” is on your side: your downstream code must be ready to handle the new shape.

---

4. Two patterns you’ll probably want

For your use case, these two patterns are clean and future-proof:

Pattern A – Independent calls with different system prompts

Exactly what we’ve been describing:

• extract_ingredients() → system = ingredient schema
• normalize_steps() → system = steps schema
• generate_podcast_script() → system = tone/style/section guidelines

Each function calls the API with its own messages and system.

This completely avoids any cross-task conflicts.

---

Pattern B – Conversation with a stable “persona” system prompt

If you later build an interactive console or UI where the user chats with the “neurodiversity cooking assistant”, you might do:

messages = [
    {"role": "system", "content": "You are a friendly recipe coach for neurodivergent people..."},
    # then user/assistant turns appended over time
]

In this mode:

• You normally don’t keep changing the system prompt mid-conversation.
• If you really need to adjust behaviour, you usually:
  • Start a new conversation with a new system prompt, or
  • Add a strong user message like: “From now on, please reply only in JSON”.

For the pipeline (batch processing recipes into audio), Pattern A is cleaner.

---

5. So, to answer your underlying worry directly

“Can we change the system prompt later without breaking/conflicting the LLM?”

Yes:

• For separate calls (like stages in your pipeline): you can change system prompts freely. That’s the intended use.
• You’re not “poisoning” or permanently altering the model. Each call is stateless except for the messages you explicitly send.
• Conflicts only appear within the same call when you mix inconsistent instructions.

If you want, next I can show you a full miniature pipeline layout like:

• extract_ingredients() → system A
• extract_steps() → system B
• generate_script() → system C

with clearly separated system prompts, so you can literally see how they stay independent and non-conflicting.

You

Before moving on in the process i have a more generic question. Instead of always defining Claude in each client. message can i define it once in the begining. Can i have it as a class? Pros and cons?

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Yeah, you don’t have to keep re-declaring everything every time. You can absolutely:

• define the Anthropic client once, and
• wrap “talk to Claude” in a class or helper functions.

Let’s walk through options + pros/cons, then I’ll show you a clean class pattern that fits your pipeline idea.

---

1. Quick reminder: what you have now

Roughly:

import anthropic, os

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

message = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1000,
    messages=[...],
)

This works but:

• You repeat model="claude-sonnet-4-5" everywhere
• Prompt wiring + response parsing gets duplicated

---

2. Option A – Simple module-level helper (no class)

This is the minimal “don’t overcomplicate yet” version:

llm_client.py

import os
import anthropic

ANTHROPIC_MODEL = "claude-sonnet-4-5"

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

def call_claude(messages, max_tokens=1000, model: str | None = None):
    resp = client.messages.create(
        model=model or ANTHROPIC_MODEL,
        max_tokens=max_tokens,
        messages=messages,
    )
    text = "".join(
        block.text for block in resp.content if block.type == "text"
    )
    return text

Then:

from llm_client import call_claude

answer = call_claude(
    messages=[{"role": "user", "content": "Hello, Claude"}]
)

Pros

• Super simple
• Model + key in one place (ANTHROPIC_MODEL, env var)
• Easy to change the model later

Cons

• If you want different “modes” (ingredient extractor, script writer, QA), you still need to manage their prompts elsewhere.
• Global client makes testing slightly less nice (but not awful).

This is often enough for small scripts.

---

3. Option B – A small class wrapper

This is what you were hinting at: keep Claude config and common behaviour together.

llm_client.py

import os
import json
import anthropic

class ClaudeClient:
    def __init__(
        self,
        api_key: str | None = None,
        default_model: str = "claude-sonnet-4-5",
    ):
        self.client = anthropic.Anthropic(api_key=api_key or os.environ["ANTHROPIC_API_KEY"])
        self.default_model = default_model

    def ask(
        self,
        messages: list[dict],
        model: str | None = None,
        max_tokens: int = 1000,
    ) -> str:
        """Generic text-in / text-out call."""
        resp = self.client.messages.create(
            model=model or self.default_model,
            max_tokens=max_tokens,
            messages=messages,
        )
        return "".join(
            block.text for block in resp.content if block.type == "text"
        )

    # Example: specialized helper for ingredients
    def extract_ingredients(self, recipe_text: str) -> dict:
        system = "... your ingredient system prompt ..."
        user = f'Extract the ingredients from this recipe:\n""" {recipe_text} """'

        raw = self.ask(
            messages=[
                {"role": "system", "content": system},
                {"role": "user", "content": user},
            ],
            max_tokens=1000,
        )

        data = json.loads(raw)
        return data

    # You can add generate_script(), check_consistency(), etc.

Usage:

from llm_client import ClaudeClient

claude = ClaudeClient(default_model="claude-sonnet-4-5")

ingredients = claude.extract_ingredients(recipe_text)
print(ingredients)

# or generic:
answer = claude.ask([{"role": "user", "content": "Hello"}])

Pros

• Central config
  • API key, default model, maybe default max_tokens, temperature, etc., all in one place.
• Easy to swap models/providers
  • Change "claude-sonnet-4-5" to something else → whole app follows.
• Nice place for specialized methods
  • extract_ingredients, normalize_steps, generate_podcast_script, qa_check as methods.
• Better testability:
  • You can inject a fake client in tests (FakeClaudeClient).

Cons

• Slightly more boilerplate up front.
• If you’re doing a tiny script or two, it’s arguably overkill at first.
• If you start adding state (like conversation history) inside the class, you have to think a bit about concurrency and lifetime (per request? per user? per process?).

For your pipeline (multiple structured steps, all calling Claude) a small class is actually a really comfortable pattern.

---

4. When class vs simple function matters

Use a class when:

• You have multiple distinct behaviours (ingredient extraction, script generation, QA) that all hit the same LLM.
• You might want to:
  • swap models (Sonnet ↔ Haiku)
  • add logging / metrics / retry around calls
• You care about testability and clean architecture.

Use plain functions when:

• It’s a small script or quick prototype.
• You don’t mind some duplication.
• You don’t expect many different call types.

You can also start with a simple function and refactor into a class later without much pain.

---

5. One hybrid I really like for your use case

You can combine both ideas:

llm_client.py

import os
import json
import anthropic

class ClaudeClient:
    def __init__(
        self,
        api_key: str | None = None,
        model_ingredients: str = "claude-haiku-4-5",
        model_scripts: str = "claude-sonnet-4-5",
    ):
        self.client = anthropic.Anthropic(api_key=api_key or os.environ["ANTHROPIC_API_KEY"])
        self.model_ingredients = model_ingredients
        self.model_scripts = model_scripts

    def _call(self, messages, model, max_tokens=1000) -> str:
        resp = self.client.messages.create(
            model=model,
            max_tokens=max_tokens,
            messages=messages,
        )
        return "".join(
            block.text for block in resp.content if block.type == "text"
        )

    def extract_ingredients(self, recipe_text: str) -> dict:
        # system + user prompts for ingredients...
        ...
        raw = self._call(messages, model=self.model_ingredients)
        return json.loads(raw)

    def generate_podcast_script(self, recipe_json: dict) -> str:
        # system + user prompts for script...
        ...
        return self._call(messages, model=self.model_scripts)

Now:

• Ingredients go to a cheaper/faster model (Haiku).
• Script-writing goes to the nicer one (Sonnet).
• You can tweak those model choices in one place.

---

6. TL;DR

• You don’t have to re-specify Claude everywhere; you can absolutely centralize it.
• A class wrapper (ClaudeClient) is a good idea for your multi-step pipeline:
  • Shared config (key, models)
  • Shared error handling and response parsing
  • One place to hang specialized methods like extract_ingredients()
• The downside is just a bit more upfront structure, but it pays off quickly as your project grows.

If you want, I can help you sketch a small ClaudeClient file and then actual pipeline.py functions that use it for:

1. extract_ingredients
2. extract_steps
3. generate_podcast_script

so your whole flow is cleanly organized.

You

What’s the difference between these two options you provided

def ask( self, messages: list[dict], model: str | None = None, max_tokens: int = 1000, ) -> str: """Generic text-in / text-out call.""" resp = self.client.messages.create( model=model or self.default_model, max_tokens=max_tokens, messages=messages, ) return "".join( block.text for block in resp.content if block.type == “text” )

def _call(self, messages, model, max_tokens=1000) -> str: resp = self.client.messages.create( model=model, max_tokens=max_tokens, messages=messages, ) return "".join( block.text for block in resp.content if block.type == “text” )

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Good eye — they look almost identical, but they’re meant for slightly different roles in the class.

Here’s the key idea:

• ask(...) → public, general-purpose helper with a default model
• _call(...) → private, low-level helper used inside specialized methods

Let’s break it down.

---

1. ask(...) – “hey Claude, answer this arbitrary thing”

def ask(
        self,
        messages: list[dict],
        model: str | None = None,
        max_tokens: int = 1000,
    ) -> str:
    """Generic text-in / text-out call."""
    resp = self.client.messages.create(
        model=model or self.default_model,
        max_tokens=max_tokens,
        messages=messages,
    )
    return "".join(
        block.text for block in resp.content if block.type == "text"
    )

Characteristics:

• Intended to be called from outside the class.
• Has a default model (self.default_model) if you don’t pass one.
• It’s your “just send some messages and give me text” utility.

Use it when you want to do one-off or ad-hoc calls in your code:

claude = ClaudeClient(default_model="claude-sonnet-4-5")

answer = claude.ask([
    {"role": "user", "content": "Explain mise en place simply."}
])

So: public, convenient, flexible.

---

2. _call(...) – “internal raw API wrapper”

def _call(self, messages, model, max_tokens=1000) -> str:
    resp = self.client.messages.create(
        model=model,
        max_tokens=max_tokens,
        messages=messages,
    )
    return "".join(
        block.text for block in resp.content if block.type == "text"
    )

Characteristics:

• The leading underscore (_call) is a Python convention:
  ➜ “this is an internal method, not part of the public API”.
• It doesn’t choose a default model. You must pass one.
  That’s intentional: your higher-level methods decide which model to use.
• Typically used only by other methods in the class, like:

def extract_ingredients(self, recipe_text: str) -> dict:
    system = "...ingredients system prompt..."
    user = f"...{recipe_text}..."
    messages = [
        {"role": "system", "content": system},
        {"role": "user", "content": user},
    ]
    raw = self._call(messages, model=self.model_ingredients)
    return json.loads(raw)

So: low-level, explicit, used as a building block.

---

3. When to use which pattern?

Think of two layers:

Layer 1 – Generic “chat with Claude”

You only need one model most of the time, and you might use the client in multiple places:

➡️ Use ask(...) with a default model.

Good for:

• quick experiments
• small scripts
• REPL-like usage

Layer 2 – Task-specific pipeline

You have distinct tasks that might use different models:

• model_ingredients (cheaper/faster)
• model_scripts (nicer writing)

And specialized methods:

• extract_ingredients()
• generate_podcast_script()
• qa_check()

These methods know exactly which model to use, so inside them you call _call(...) and explicitly pass model=self.model_ingredients or self.model_scripts.

➡️ In that setup, _call is the small shared “talk to Anthropic” primitive.

---

4. You can also mix them

In the same class you might reasonably have:

• _call(...) for internal usage from specialized methods.
• ask(...) as a convenience for you when doing ad-hoc stuff.

Example:

def ask(self, prompt: str, model: str | None = None) -> str:
    messages = [{"role": "user", "content": prompt}]
    return self._call(messages, model or self.default_model)

So ask becomes a thin “nice interface” around _call.

---

TL;DR

• The two functions do the same low-level thing (send messages, get text).
• The difference is about how you structure your code:
  • ask(...) – public, flexible, uses a default model.
  • _call(...) – internal utility, always explicit about which model is used, called by higher-level methods like extract_ingredients().

You’re not wrong to spot the similarity — the distinction is more architectural than functional.

You

so their main difference is that in call you define the model?

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Pretty much, yeah — that’s the core practical difference in the examples I gave, but there’s a bit of nuance:

• In ask(...):

  • model is optional.
  • If you don’t pass it, it falls back to self.default_model.
  • It’s meant as a public, convenient helper you call from outside.

  def ask(self, messages, model: str | None = None, max_tokens=1000):
      resp = self.client.messages.create(
          model=model or self.default_model,  # <-- default here
          max_tokens=max_tokens,
          messages=messages,
      )

• In _call(...):

  • model is required – no default or fallback.
  • It’s meant as a private, low-level helper that other methods in the class use.

  def _call(self, messages, model, max_tokens=1000):
      resp = self.client.messages.create(
          model=model,  # <-- must be explicit
          max_tokens=max_tokens,
          messages=messages,
      )

So:

• Functionally they both send a request and return text.
• Design-wise:
  • ask = “I’ll pick a sensible default model if you don’t care.”
  • _call = “You must tell me which model; I’m just a thin wrapper around the SDK.”

You can absolutely simplify and only keep one of them if you like. For your project, a nice pattern is:

def _call(self, messages, model=None, max_tokens=1000):
    resp = self.client.messages.create(
        model=model or self.default_model,
        max_tokens=max_tokens,
        messages=messages,
    )
    return "".join(block.text for block in resp.content if block.type == "text")

Then use _call internally from methods like extract_ingredients / generate_script, and maybe also expose a small ask(prompt: str) that just wraps _call.

You

messages always need to be in the dictionary format? because we define role and content?

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Yeah, with the current Anthropic Messages API, messages always needs to be a list of “message” objects, and each message object needs at least:

{"role": "...", "content": "..."}

So in Python that means: a list of dicts.

Example:

messages = [
    {"role": "user", "content": "Explain mise en place simply."}
]

is the minimum valid shape.

---

Why it’s like this

The API is designed to support multi-turn conversations, not just a single prompt string. So it needs to know:

• who said each thing → role ("system" | "user" | "assistant")
• what they said → content (the actual text, or richer “blocks”)

Behind the scenes, the “real” spec is:

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Explain mise en place simply."}
        ]
    }
]

But the Python SDK lets you use a plain string for content as a shortcut, and it wraps it into a text block for you.

So all of these are valid:

# 1) Simple one-turn chat
messages=[{"role": "user", "content": "Hello"}]

# 2) With a system prompt
messages=[
    {"role": "system", "content": "You are a cooking assistant."},
    {"role": "user", "content": "Hello"},
]

# 3) Fully explicit content blocks
messages=[
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Hello"}
        ]
    }
]

What you can’t do is pass just a raw string like:

messages="Hello"        # ❌ not valid
messages=["Hello"]      # ❌ not valid

because the API wouldn’t know which role that belongs to.

---

If you don’t want to think about dicts all the time

You can hide this behind a helper:

def simple_user_prompt(text: str):
    return [{"role": "user", "content": text}]

resp = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=500,
    messages=simple_user_prompt("Explain mise en place simply."),
)

Or in your ClaudeClient:

def ask(self, prompt: str, model: str | None = None, max_tokens: int = 1000) -> str:
    messages = [{"role": "user", "content": prompt}]
    ...

So yes, the underlying API always wants that “list of role/content objects”, but you don’t have to manually write dicts everywhere if you wrap it nicely.

You

ok i got an error about sytem. I think maybe you have been mistaken there. Here is my class

import os import json import anthropic

class ClaudeClient:

def __init__(
        self,
        api_key: str | None = None,
        default_model: str = "claude-sonnet-4-5",
):
    self.client = anthropic.Anthropic(api_key=api_key or os.environ["ANTHROPIC_API_KEY"])
    self.default_model = default_model

def call(
        self,
        messages: list[dict],
        model: str | None = None,
        max_tokens: int = 1000,
) -> str:
    """Generic text-in / text-out call."""
    resp = self.client.messages.create(
        model=model or self.default_model,
        max_tokens=max_tokens,
        messages=messages,
    )
    return "".join(
        block.text for block in resp.content if block.type == "text"
    )

def create_message(self, system_prompt: str, context_prompt: str) -> list[dict]:
    message = []
    if system_prompt == None:
        pass
    else:
        sys = {"role": "system", "content": system_prompt}
        message.append(sys)

    if context_prompt==None:
        pass
    else:
        cont = {"role": "user", "content": context_prompt}
        message.append(cont)

    return message

and the implementation

import anthropic from dotenv import load_dotenv import os import yaml from llm_client import ClaudeClient

-------------- GET ENVIRONMENT VARIABLES ------------------

load_dotenv() CLAUDE_KEY = os.getenv(“ANTHROPIC_API_KEY”)

-------------- LOAD CONFIG YAML ------------------

with open(“recipe_config.yml”, “r”, encoding=“utf-8”) as file: config = yaml.safe_load(file)

-------------- CREATE CLAUDE CLIENT ------------------

#client = anthropic.Anthropic(api_key=CLAUDE_KEY) claude = ClaudeClient()

-------------- CREATE INGREDIENTS PROMPT ------------------

ing_sys = config[“system_prompts”][“ingredients”] ing_ctx = config[“context_prompts”][“ingredients”]

ing_msg = claude.create_message(ing_sys, ing_ctx) print(ing_msg)

===================================================

============== RUN CLAUDE =========================

===================================================

-------------- STEP 1|INGREDIENTS ------------------

response = claude.call(messages=ing_msg) print(response)

I am getting this error:

Traceback (most recent call last): File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\claude_chat.py”, line 32, in response = claude.call(messages=ing_msg) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\llm_client.py”, line 22, in call resp = self.client.messages.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic_utils_utils.py”, line 282, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic\resources\messages\messages.py”, line 930, in create return self._post( ^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic_base_client.py”, line 1326, in post return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic_base_client.py”, line 1114, in request raise self._make_status_error_from_response(err.response) from None anthropic.BadRequestError: Error code: 400 - {‘type’: ‘error’, ‘error’: {‘type’: ‘invalid_request_error’, ‘message’: ‘messages: Unexpected role “system”. The Messages API accepts a top-level system parameter, not “system” as an input message role.’}, ‘request_id’: ‘req_011CVw69wj1NHPAXKBzEmFVu’}

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Nice, thanks for sharing the code + traceback, that helps a lot.

The error is actually very clear (once you know what to look for):

Unexpected role "system". The Messages API accepts a top-level system parameter, not "system" as an input message role.

Anthropic’s new Messages API does not accept "role": "system" inside messages.
Instead, it wants a top-level system= argument.

So yeah: this part I previously suggested is outdated for the current SDK – sorry about that, and nice catch.

Let’s fix your class.

---

1. Why it’s failing

You currently build messages like:

sys = {"role": "system", "content": system_prompt}
message.append(sys)

Then call:

resp = self.client.messages.create(
    model=model or self.default_model,
    max_tokens=max_tokens,
    messages=messages,
)

The SDK now expects:

resp = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=...,
    system="your system prompt here",     # <— top-level
    messages=[{"role": "user", "content": "..."}]
)

So:

• system → top-level arg
• messages → only "user" and "assistant" roles

---

2. Minimal fix to your class

Step 1: change call to accept an optional system_prompt

class ClaudeClient:

    def __init__(
            self,
            api_key: str | None = None,
            default_model: str = "claude-sonnet-4-5",
    ):
        self.client = anthropic.Anthropic(api_key=api_key or os.environ["ANTHROPIC_API_KEY"])
        self.default_model = default_model

    def call(
            self,
            messages: list[dict],
            system_prompt: str | None = None,
            model: str | None = None,
            max_tokens: int = 1000,
    ) -> str:
        """Generic text-in / text-out call."""
        resp = self.client.messages.create(
            model=model or self.default_model,
            max_tokens=max_tokens,
            system=system_prompt,          # <-- HERE: top-level system
            messages=messages,             # only user/assistant roles
        )
        return "".join(
            block.text for block in resp.content if block.type == "text"
        )

Step 2: change create_message to only build user messages

You don’t want to put system inside messages anymore.

    def create_message(self, context_prompt: str | None) -> list[dict]:
        messages: list[dict] = []

        if context_prompt is not None:
            messages.append({"role": "user", "content": context_prompt})

        return messages

(You don’t need system_prompt here at all now.)

---

3. Updated usage

Given your config:

ing_sys = config["system_prompts"]["ingredients"]
ing_ctx = config["context_prompts"]["ingredients"]

ing_msg = claude.create_message(ing_ctx)
print(ing_msg)

response = claude.call(
    messages=ing_msg,
    system_prompt=ing_sys,   # <-- pass system here
)
print(response)

That matches how Anthropic wants it:

• system=ing_sys
• messages=[{"role": "user", "content": ing_ctx}]

---

4. If you want to simplify further

Honestly, since you’re always doing “system + one user prompt” for this step, you can even skip create_message completely and do:

response = claude.call(
    messages=[{"role": "user", "content": ing_ctx}],
    system_prompt=ing_sys,
)

Or create a tiny convenience method:

def call_with_system(self, system_prompt: str, user_prompt: str, **kwargs) -> str:
    messages = [{"role": "user", "content": user_prompt}]
    return self.call(messages=messages, system_prompt=system_prompt, **kwargs)

Then:

response = claude.call_with_system(ing_sys, ing_ctx)

---

5. Quick mental model

• Old thinking (OpenAI-style): system/user/assistant all live in messages.
• Anthropic Messages API now:
  • system is separate, top-level, not a role in the messages list.
  • messages is just the conversation turns (user / assistant).

Once you wire that in, your class design is fine.

If you paste one of your ingredients system + context prompts, I can show you exactly how I’d call your new ClaudeClient to get back that JSON we designed earlier.

You

ok i did the amendments. I just want to prompt the user in the python console to write the recipe

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Nice, you’re really close now.
What you want is basically:

1. Ask the user for a recipe in the console
2. Build a user prompt that includes that recipe text
3. Call your ClaudeClient with system_prompt + messages
4. Print the JSON that Claude returns

I’ll show you a clean way to do that, including multiline paste support (so you can paste a whole recipe easily).

---

1. Helper: read a recipe from the console (multiline)

This lets you paste multiple lines and finish with a blank line:

def read_recipe_from_console() -> str:
    print("Paste your recipe below.")
    print("When you're done, press ENTER on an empty line to finish.\n")

    lines = []
    while True:
        try:
            line = input()
        except EOFError:
            # In case of Ctrl+D / Ctrl+Z
            break

        if line.strip() == "":  # empty line = done
            break

        lines.append(line)

    recipe_text = "\n".join(lines)
    return recipe_text

---

2. Using your ClaudeClient with system + user prompt

Given your updated class:

class ClaudeClient:
    def __init__(
            self,
            api_key: str | None = None,
            default_model: str = "claude-sonnet-4-5",
    ):
        self.client = anthropic.Anthropic(api_key=api_key or os.environ["ANTHROPIC_API_KEY"])
        self.default_model = default_model

    def call(
            self,
            messages: list[dict],
            system_prompt: str | None = None,
            model: str | None = None,
            max_tokens: int = 1000,
    ) -> str:
        resp = self.client.messages.create(
            model=model or self.default_model,
            max_tokens=max_tokens,
            system=system_prompt,
            messages=messages,
        )
        return "".join(
            block.text for block in resp.content if block.type == "text"
        )

You no longer need create_message for this simple case – you can just build the user message directly.

Assuming your YAML has:

system_prompts:
  ingredients: "your long system prompt here..."

context_prompts:
  ingredients: "Extract the ingredients into JSON with this format: ..."

You can do:

import os
import yaml
from dotenv import load_dotenv
from llm_client import ClaudeClient

load_dotenv()

# -------------- LOAD CONFIG YAML ------------------
with open("recipe_config.yml", "r", encoding="utf-8") as file:
    config = yaml.safe_load(file)

ing_sys = config["system_prompts"]["ingredients"]      # system schema / rules
ing_ctx_template = config["context_prompts"]["ingredients"]  # e.g. "Extract ingredients from this recipe:"

def read_recipe_from_console() -> str:
    print("Paste your recipe below.")
    print("When you're done, press ENTER on an empty line to finish.\n")

    lines = []
    while True:
        try:
            line = input()
        except EOFError:
            break

        if line.strip() == "":
            break

        lines.append(line)

    return "\n".join(lines)

def main():
    claude = ClaudeClient()

    recipe_text = read_recipe_from_console()

    # Build the full user prompt by combining your context prompt + the pasted recipe
    user_prompt = f"""{ing_ctx_template}

RECIPE:
\"\"\"{recipe_text}\"\"\""""

    messages = [
        {"role": "user", "content": user_prompt}
    ]

    response = claude.call(
        messages=messages,
        system_prompt=ing_sys,   # <--- system goes here now
        max_tokens=1000,
    )

    print("\n=== Claude response ===\n")
    print(response)

if __name__ == "__main__":
    main()

What this does:

1. Loads your system/context prompts from YAML.

2. Prompts you in the console to paste the recipe (end with blank line).

3. Builds a user message like:

   <context prompt from YAML>
   
   RECIPE:
   """<recipe you pasted>"""

4. Calls Claude with:

   • system = ing_sys
   • messages = [{"role": "user", "content": user_prompt}]

5. Prints the JSON string Claude returns (you can then json.loads it if you want to use it programmatically).

---

If you want, next we can add one more tiny step on top of this: immediately json.loads(response) and pretty-print the parsed ingredients so you can see them nicely in the console.

You

I now get this error

Paste your recipe below. When you’re done, press ENTER on an empty line to finish.

1 butternut squash, peeled and chopped into 2 cm pieces 3 tbsp olive oil 1/2 onion finely chopped 1/2 celery stick finely chopped 2 garlic cloves, crushed 1 Knorr Vegetable Stock Pot 1 bay leaf 1 tsp fresh thyme leaves 140 g Arborio rice 70 ml white wine 500 ml boiling water 25 g Flora Buttery 50 g Parmesan, grated or a similar vegetarian substitute)

Cooking instructions Preheat the oven to 180°C, 160°C fan, Gas mark 4.

Place butternut squash in a baking tray and drizzle with 2 tablespoons of olive oil. Bake for 15-20 minutes, until tender. Set aside.

In a wide based pan, heat the remaining oil over medium heat. Add the onion and celery. Cook for 5 minutes, until vegetables have softened. Add the garlic and cook for 2 minutes.

Add the Knorr Vegetable Stock Pot, bay leaf, thyme and rice. Cook for 3 minutes, until the rice is coated well in the oil. Add the wine and stir until all of the liquid has absorbed into the rice.

Add 100ml of boiling water to the rice and stir continuously, until all the liquid has been absorbed. Repeat this process three or more times, and cook until the rice is tender.

To finish, add the Flora Buttery and grated Parmesan. Fold in the roasted butternut squash[{‘role’: ‘user’, ‘content’: ‘Extract the ingredients from the following recipe text into the JSON format described.\n\nRECIPE TEXT:1 butternut squash, peeled and chopped into 2 cm pieces\n3 tbsp olive oil\n1/2 onion finely chopped\n1/2 celery stick finely chopped\n2 garlic cloves, crushed\n1 Knorr Vegetable Stock Pot\n1 bay leaf\n1 tsp fresh thyme leaves\n140 g Arborio rice\n70 ml white wine\n500 ml boiling water\n25 g Flora Buttery\n50 g Parmesan, grated or a similar vegetarian substitute)\n’}] Traceback (most recent call last): File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\claude_chat.py”, line 57, in response = claude.call(messages=ing_msg) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\llm_client.py”, line 23, in call resp = self.client.messages.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic_utils_utils.py”, line 282, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic\resources\messages\messages.py”, line 930, in create return self._post( ^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic_base_client.py”, line 1326, in post return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\site-packages\anthropic_base_client.py”, line 1114, in request raise self._make_status_error_from_response(err.response) from None anthropic.BadRequestError: Error code: 400 - {‘type’: ‘error’, ‘error’: {‘type’: ‘invalid_request_error’, ‘message’: ‘system: Input should be a valid list’}, ‘request_id’: ‘req_011CVw85dE9CXyDcbzkSBS9K’}

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Nice, this one’s a subtle but very fixable bug 👍

The key bit from the error:

system: Input should be a valid list

What’s happening:

• In your ClaudeClient.call method, you’re always passing system=system_prompt to messages.create.
• When you call claude.call(messages=ing_msg) you don’t pass system_prompt, so it defaults to None.
• The Anthropic SDK then sees system=None and complains, because it expects either:
  • no system argument at all, or
  • a valid value (string or list of blocks, depending on SDK version).

So the problem is: you’re passing system=None down to the SDK.

---

Fix: only send system when you actually have one

Update your call method to conditionally include system:

class ClaudeClient:

    def __init__(
            self,
            api_key: str | None = None,
            default_model: str = "claude-sonnet-4-5",
    ):
        self.client = anthropic.Anthropic(
            api_key=api_key or os.environ["ANTHROPIC_API_KEY"]
        )
        self.default_model = default_model

    def call(
            self,
            messages: list[dict],
            system_prompt: str | None = None,
            model: str | None = None,
            max_tokens: int = 1000,
    ) -> str:
        """Generic text-in / text-out call."""
        kwargs = {
            "model": model or self.default_model,
            "max_tokens": max_tokens,
            "messages": messages,
        }

        # ✅ Only include system if we actually have a prompt
        if system_prompt is not None:
            kwargs["system"] = system_prompt

        resp = self.client.messages.create(**kwargs)

        return "".join(
            block.text for block in resp.content if block.type == "text"
        )

Now:

• If you call claude.call(messages=ing_msg) → no system is sent at all.
• If you call claude.call(messages=ing_msg, system_prompt=ing_sys) → system=ing_sys is sent correctly.

---

How to use it with your recipe prompt

Given what you printed:

ing_msg = [
    {
        'role': 'user',
        'content': 'Extract the ingredients from the following recipe text into the JSON format described.\n\nRECIPE TEXT:1 butternut squash, peeled and chopped into 2 cm pieces\n...'
    }
]

Call it like:

response = claude.call(
    messages=ing_msg,
    system_prompt=ing_sys,  # from your config
    max_tokens=1000,
)
print(response)

or, if you don’t want a system prompt for some reason:

response = claude.call(messages=ing_msg)

Both will now work without the system: Input should be a valid list error.

---

If you paste your current ing_sys and ing_ctx contents at some point, I can help you tighten those prompts specifically for JSON extraction too.

You

I think you got it all wrong re system

Traceback (most recent call last): File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\claude_chat.py”, line 57, in response = claude.call(system=ing_sys, messages=ing_msg) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: ClaudeClient.call() got an unexpected keyword argument ‘system’

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Ah, this one is much simpler than it looks 🙂
Nothing is “wrong” with Anthropic’s system – this error is coming from your class API, not from Claude.

You wrote:

response = claude.call(system=ing_sys, messages=ing_msg)

But your ClaudeClient.call method is defined as:

def call(
        self,
        messages: list[dict],
        system_prompt: str | None = None,
        model: str | None = None,
        max_tokens: int = 1000,
) -> str:
    ...

So the parameter is called system_prompt, not system.
Python sees system= and goes: “I don’t know this argument”, hence:

TypeError: ClaudeClient.call() got an unexpected keyword argument 'system'

---

1. Quick fix

Just change your call site to use the correct parameter name:

response = claude.call(
    messages=ing_msg,
    system_prompt=ing_sys,  # <-- use system_prompt here
)

That matches your method signature and keeps system as the top-level parameter when actually calling Anthropic.

---

2. Make sure your call method only sends system when present

Your call method should look like this (the version I suggested last):

class ClaudeClient:

    def __init__(
            self,
            api_key: str | None = None,
            default_model: str = "claude-sonnet-4-5",
    ):
        self.client = anthropic.Anthropic(
            api_key=api_key or os.environ["ANTHROPIC_API_KEY"]
        )
        self.default_model = default_model

    def call(
            self,
            messages: list[dict],
            system_prompt: str | None = None,
            model: str | None = None,
            max_tokens: int = 1000,
    ) -> str:
        """Generic text-in / text-out call."""
        kwargs = {
            "model": model or self.default_model,
            "max_tokens": max_tokens,
            "messages": messages,
        }

        # only include system if we actually have one
        if system_prompt is not None:
            kwargs["system"] = system_prompt

        resp = self.client.messages.create(**kwargs)

        return "".join(
            block.text for block in resp.content if block.type == "text"
        )

So:

• Your code exposes system_prompt=...
• The Anthropic SDK receives system=... internally via kwargs.

---

3. Example end-to-end with your current flow

Putting it together with your ing_sys / ing_msg:

ing_sys = config["system_prompts"]["ingredients"]
ing_ctx = config["context_prompts"]["ingredients"]

# build the user message
ing_msg = [
    {
        "role": "user",
        "content": f"""{ing_ctx}

RECIPE TEXT:
\"\"\"{recipe_text}\"\"\""""
    }
]

response = claude.call(
    messages=ing_msg,
    system_prompt=ing_sys,  # <-- this is the important bit
)

print(response)

That’s all you need: the main confusion was just the parameter name mismatch (system vs system_prompt).

If you’d prefer to think in terms of system everywhere, you can absolutely rename the argument in your class from system_prompt to system – just make sure the call site and the method definition match.

You

right…Ok this is the response I am getting

{
  "ingredients": [
    {
      "name": "butternut squash",
      "quantity": "1",
      "preprocess": "peeled and chopped into 2 cm pieces",
      "original_text": "1 butternut squash, peeled and chopped into 2 cm pieces",
      "group": null
    },
    {
      "name": "olive oil",
      "quantity": "3 tbsp",
      "preprocess": null,
      "original_text": "3 tbsp olive oil",
      "group": null
    },
    {
      "name": "onion",
      "quantity": "1/2",
      "preprocess": "finely chopped",
      "original_text": "1/2 onion finely chopped",
      "group": null
    },
    {
      "name": "celery stick",
      "quantity": "1/2",
      "preprocess": "finely chopped",
      "original_text": "1/2 celery stick finely chopped",
      "group": null
    },
    {
      "name": "garlic cloves",
      "quantity": "2",
      "preprocess": "crushed",
      "original_text": "2 garlic cloves, crushed",
      "group": null
    },
    {
      "name": "Knorr Vegetable Stock Pot",
      "quantity": "1",
      "preprocess": null,
      "original_text": "1 Knorr Vegetable Stock Pot",
      "group": null
    },
    {
      "name": "bay leaf",
      "quantity": "1",
      "preprocess": null,
      "original_text": "1 bay leaf",
      "group": null
    },
    {
      "name": "fresh thyme leaves",
      "quantity": "1 tsp",
      "preprocess": null,
      "original_text": "1 tsp fresh thyme leaves",
      "group": null
    },
    {
      "name": "Arborio rice",
      "quantity": "140 g",
      "preprocess": null,
      "original_text": "140 g Arborio rice",
      "group": null
    },
    {
      "name": "white wine",
      "quantity": "70 ml",
      "preprocess": null,
      "original_text": "70 ml white wine",
      "group": null
    },
    {
      "name": "boiling water",
      "quantity": "500 ml",
      "preprocess": null,
      "original_text": "500 ml boiling water",
      "group": null
    },
    {
      "name": "Flora Buttery",
      "quantity": "25 g",
      "preprocess": null,
      "original_text": "25 g Flora Buttery",
      "group": null
    },
    {
      "name": "Parmesan",
      "quantity": "50 g",
      "preprocess": "grated or a similar vegetarian substitute",
      "original_text": "50 g Parmesan, grated or a similar vegetarian substitute)",
      "group": null
    }
  ]
}

I think this be should not be there '''json ''' right?

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Yep, you’re absolutely right – for your use case those:

```json
...

code fences should **not** be there. You want **pure JSON**, nothing wrapped in markdown.

Right now Claude is doing the “helpful dev” thing and wrapping the JSON in a code block. Two parts to fixing this:

---

## 1. Fix the *prompt* so it asks for raw JSON only

In your **system prompt** (the `ing_sys` from YAML), make sure you have something very explicit, like:

```text
Output format:
- Respond with a SINGLE valid JSON object.
- No code fences.
- No Markdown.
- No explanations.
- No text before or after the JSON.

If you don’t already have the “No code fences / No Markdown” bit in there, add it. That usually fixes 90% of cases.

---

2. Be robust in your Python anyway

Even with a good prompt, models sometimes still sneak in fences. Since you’ll always parse this with json.loads, it’s smart to strip them defensively.

Something like:

raw_text = claude.call(...)

# 1) strip common code fences if present
if raw_text.strip().startswith("```"):
    # split on ``` and take the middle bit
    parts = raw_text.split("```")
    # parts might be: ["", "json\n{...}", ""]
    if len(parts) >= 2:
        raw_text = parts[1]
    # remove leading language tag like "json\n"
    raw_text = raw_text.lstrip("json").lstrip("\n")

# 2) optionally trim to the JSON object from first { to last }
start = raw_text.find("{")
end = raw_text.rfind("}")
if start != -1 and end != -1:
    raw_text = raw_text[start : end + 1]

import json
data = json.loads(raw_text)

That way:

• If Claude behaves and returns pure JSON → json.loads just works.
• If it wraps in json ... → you still recover cleanly.

---

So: you’re correct, the ```json fences shouldn’t be there for your pipeline.
Tweak the system prompt to forbid markdown, and add a small “de-fencing” step before json.loads so your code is solid even when the model slips.

You

ok now that I have my json with the basic ingredients, I’d like to do a pass where based on each ingredient and its preprocess (if any), ask Claude to propose a tool or tools for tha pre-process step. This is definitely something that would come up as instruction in a listening narrated version of a recipe.

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Nice, this is a great next step for making the audio narration richer and more supportive. Let’s wire it up cleanly.

You already have this JSON:

{
  "ingredients": [
    {
      "name": "butternut squash",
      "quantity": "1",
      "preprocess": "peeled and chopped into 2 cm pieces",
      "original_text": "1 butternut squash, peeled and chopped into 2 cm pieces",
      "group": null
    },
    ...
  ]
}

Now you want:

for each ingredient + preprocess, propose tool(s) that would realistically be used for that prep step.

---

1. Data shape: where do “tools” live?

Simplest and most robust: extend your existing ingredients with a tools field, instead of creating a separate mapping.

That way each ingredient becomes:

{
  "name": "butternut squash",
  "quantity": "1",
  "preprocess": "peeled and chopped into 2 cm pieces",
  "original_text": "1 butternut squash, peeled and chopped into 2 cm pieces",
  "group": null,
  "tools": [
    {
      "name": "chef's knife",
      "purpose": "chopping the squash into 2 cm pieces"
    },
    {
      "name": "cutting board",
      "purpose": "safe, stable surface for chopping"
    },
    {
      "name": "vegetable peeler",
      "purpose": "peeling the squash before chopping"
    }
  ]
}

Why this is nice:

• You keep a single source of truth per ingredient.
• Later, when generating the audio script, you can say:
  • “For the butternut squash, you’ll need: a chef’s knife, a cutting board, and a vegetable peeler.”
• Programmatically trivial: no need to join multiple arrays; everything is right there.

---

2. System prompt for “add tools to ingredients”

You’ll give Claude:

• The ingredients JSON (from step 1).
• A system prompt that says “keep everything, just add tools”.

Example system prompt (you can put this in your YAML under system_prompts.tools or similar):

You are an expert cooking assistant that suggests appropriate kitchen tools
for recipe ingredients based on their preparation.

You will receive a JSON object with this structure:

{
  "ingredients": [
    {
      "name": "string",
      "quantity": "string",
      "preprocess": "string or null",
      "original_text": "string",
      "group": "string or null"
    }
  ]
}

Your task:
- For each ingredient, suggest a small set of common kitchen tools that a home cook
  would reasonably use for the specified preprocess step.
- Add a new field "tools" to each ingredient entry.
- "tools" must be a list of objects with this shape:

  "tools": [
    {
      "name": "string",   // short tool name, e.g. "chef's knife", "saucepan"
      "purpose": "string" // what the tool is used for in this context
    }
  ]

Guidelines:
- If "preprocess" is null and no obvious tool is needed yet, use an empty list for "tools".
- Prefer standard, widely available tools (chef's knife, cutting board, saucepan, mixing bowl, whisk, wooden spoon).
- Avoid super-specific gadgets unless truly necessary.
- Keep tool lists short and practical (typically 1–3 tools per ingredient).
- Do not remove or rename any existing fields.
- Do not change values for "name", "quantity", "preprocess", "original_text", or "group".

Output format:
- Respond with a SINGLE valid JSON object.
- It must have the same top-level structure as the input, but with "tools" added to each ingredient.
- No code fences.
- No Markdown.
- No explanations.
- No text before or after the JSON.

---

3. User prompt for this step

User content can just be: “Here’s the JSON, apply the rules”:

def build_tools_user_prompt(ingredients_json: dict) -> str:
    import json
    return f"""
Here is the ingredient JSON:

{json.dumps(ingredients_json, ensure_ascii=False, indent=2)}

Add a "tools" list to each ingredient as described in the system prompt.
Return the full updated JSON object.
"""

---

4. A helper function using your ClaudeClient

Assuming your ClaudeClient.call looks like:

def call(
        self,
        messages: list[dict],
        system_prompt: str | None = None,
        model: str | None = None,
        max_tokens: int = 1000,
) -> str:
    kwargs = {
        "model": model or self.default_model,
        "max_tokens": max_tokens,
        "messages": messages,
    }
    if system_prompt is not None:
        kwargs["system"] = system_prompt

    resp = self.client.messages.create(**kwargs)
    return "".join(
        block.text for block in resp.content if block.type == "text"
    )

Then you can do:

import json
from llm_client import ClaudeClient

def add_tools_to_ingredients(
    claude: ClaudeClient,
    ingredients_json: dict,
    system_prompt: str,
    model: str | None = None,
) -> dict:
    user_prompt = build_tools_user_prompt(ingredients_json)

    messages = [
        {"role": "user", "content": user_prompt}
    ]

    raw = claude.call(
        messages=messages,
        system_prompt=system_prompt,
        model=model,
        max_tokens=1500,
    )

    # Optional: strip code fences defensively, in case
    if raw.strip().startswith("```"):
        parts = raw.split("```")
        if len(parts) >= 2:
            raw = parts[1]
        raw = raw.lstrip("json").lstrip("\n")

    start = raw.find("{")
    end = raw.rfind("}")
    if start != -1 and end != -1:
        raw = raw[start : end + 1]

    data = json.loads(raw)

    # Quick sanity check
    if "ingredients" not in data or not isinstance(data["ingredients"], list):
        raise ValueError("Output JSON missing 'ingredients' list")

    # Ensure each ingredient has tools
    for ing in data["ingredients"]:
        if "tools" not in ing:
            ing["tools"] = []

    return data

Usage:

# 1. You already have this from previous step:
ingredients_json = json.loads(ingredients_response_from_claude)

# 2. Load your tools system prompt from YAML
tools_sys = config["system_prompts"]["tools"]

claude = ClaudeClient()

enriched = add_tools_to_ingredients(
    claude=claude,
    ingredients_json=ingredients_json,
    system_prompt=tools_sys,
)

# See what it did
for ing in enriched["ingredients"]:
    print(ing["name"], "→ tools:")
    for tool in ing["tools"]:
        print("  -", tool["name"], ":", tool["purpose"])
    print()

---

5. Why this is useful for your narrated recipe

Once each ingredient has its tools, your later “podcast script” step can do things like:

“Before you start, prepare your tools.
For the butternut squash, you’ll need a chef’s knife, a vegetable peeler, and a cutting board.
For the onion and celery, you’ll need a chef’s knife and a cutting board.
For the risotto, you’ll also need a wide-based pan and a wooden spoon.”

That’s very helpful for neurodiverse listeners:

• reduces surprise (“oh no, I needed a peeler and I’m already mid-episode”),
• lowers cognitive load by making tools explicit up front,
• and gives you hooks to insert reassuring structure (“pause here and gather these tools…”).

If you want, next step we can design the step-normalization JSON (actions, ingredients used, tools used) and then the podcast script prompt that turns that into friendly narration ready for ElevenLabs.

You

I get this error when trying to parse the new json file

Traceback (most recent call last): File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\claude_chat.py”, line 76, in tool_json = utils.parse_json(tool_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\utils.py”, line 22, in parse_json data = json.loads(raw_text) ^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\json_init_.py”, line 346, in loads return _default_decoder.decode(s) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\json\decoder.py”, line 338, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “C:\Users\Eleana\anaconda3\envs\claude_env\Lib\json\decoder.py”, line 354, in raw_decode obj, end = self.scan_once(s, idx) ^^^^^^^^^^^^^^^^^^^^^^ json.decoder.JSONDecodeError: Expecting ’,’ delimiter: line 129 column 6 (char 3060)

here is my parsing function

def parse_json(raw_text:str):

# 1) strip common code fences if present
if raw_text.strip().startswith("```"):
    # split on ``` and take the middle bit
    parts = raw_text.split("```")
    # parts might be: ["", "json\n{...}", ""]
    if len(parts) >= 2:
        raw_text = parts[1]
    # remove leading language tag like "json\n"
    raw_text = raw_text.lstrip("json").lstrip("\n")

# 2) optionally trim to the JSON object from first { to last }
start = raw_text.find("{")
end = raw_text.rfind("}")
if start != -1 and end != -1:
    raw_text = raw_text[start : end + 1]

import json
data = json.loads(raw_text)
return data

and a sample of the file

{
  "ingredients": [
    {
      "name": "butternut squash",
      "quantity": "1",
      "preprocess": "peeled and chopped into 2 cm pieces",
      "original_text": "1 butternut squash, peeled and chopped into 2 cm pieces",
      "group": null,
      "tools": [
        {
          "name": "vegetable peeler",
          "purpose": "peeling the squash skin"
        },
        {
          "name": "chef's knife",
          "purpose": "chopping into 2 cm pieces"
        },
        {
          "name": "cutting board",
          "purpose": "stable surface for cutting"
        }
      ]
    },
    {
      "name": "olive oil",
      "quantity": "3 tbsp",
      "preprocess": null,
      "original_text": "3 tbsp olive oil",
      "group": null,
      "tools": [
        {
          "name": "measuring spoon",
          "purpose": "measuring the oil"
        }
      ]
    },
    {
      "name": "onion",
      "quantity": "1/2",
      "preprocess": "finely chopped",
      "original_text": "1/2 onion finely chopped",
      "group": null,
      "tools": [
        {
          "name": "chef's knife",
          "purpose": "finely chopping the onion"
        },
        {
          "name": "cutting board",
          "purpose": "stable surface for chopping"
        }
      ]
    },
    {
      "name": "celery stick",
      "quantity": "1/2",
      "preprocess": "finely chopped",
      "original_text": "1/2 celery stick finely chopped",
      "group": null,
      "tools": [
        {
          "name": "chef's knife",
          "purpose": "finely chopping the celery"
        },
        {
          "name": "cutting board",
          "purpose": "stable surface for chopping"
        }
      ]
    },
    {

ChatGPT

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ChatGPT

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ChatGPT

You’re doing everything right conceptually – this is just the classic “LLM JSON is almost valid but not quite” problem 🙂

The key bits:

• The traceback says:
  JSONDecodeError: Expecting ',' delimiter: line 129 column 6 (char 3060)
• The snippet you showed so far is valid JSON.
• So the actual syntax error is later in the string (after the part you pasted).

Most likely causes (from experience with these responses):

• A trailing comma before ] or }
• A missing comma between fields
• Some extra text like ... } } ] } something after the JSON
• A half-finished last ingredient (truncated list / object)

Let’s make your parser more helpful so you can see exactly what’s wrong, and then also tighten the prompt a bit.

---

1. Upgrade your parse_json to show the bad spot

Replace your parse_json with this version (or adjust yours similarly):

import json

def parse_json(raw_text: str) -> dict:
    text = raw_text.strip()

    # 1) strip common code fences if present
    if text.startswith("```"):
        parts = text.split("```")
        if len(parts) >= 2:
            text = parts[1]
        # remove leading language tag like "json\n" or "JSON\n"
        text = text.lstrip().lstrip("json").lstrip("JSON").lstrip()

    # 2) trim to the JSON object from first { to last }
    start = text.find("{")
    end = text.rfind("}")
    if start == -1 or end == -1:
        raise ValueError(f"No JSON object found in text:\n{text[:300]}...")

    text = text[start : end + 1]

    try:
        return json.loads(text)
    except json.JSONDecodeError as e:
        # Show some context around the error position to debug
        ctx_start = max(0, e.pos - 80)
        ctx_end = min(len(text), e.pos + 80)
        context = text[ctx_start:ctx_end]

        raise ValueError(
            f"Failed to parse JSON: {e.msg} (pos {e.pos})\n"
            f"Context around error:\n{context}"
        )

Now when you run:

tool_json = parse_json(tool_response)

you’ll get a clearer error with a snippet of the exact place where the JSON is broken.

From that you’ll likely see something like:

• ... } } ] } something
• or ... "purpose": "foo" "name": "bar" (missing comma)
• or ... },\n ] (trailing comma before ])

---

2. Why the snippet you showed looks fine

Everything you pasted up to:

    {
      "name": "celery stick",
      "quantity": "1/2",
      "preprocess": "finely chopped",
      "original_text": "1/2 celery stick finely chopped",
      "group": null,
      "tools": [
        {
          "name": "chef's knife",
          "purpose": "finely chopping the celery"
        },
        {
          "name": "cutting board",
          "purpose": "stable surface for chopping"
        }
      ]
    },
    {

…is valid JSON, including things like "chef's knife" (single quote inside double quotes is fine) and null.

So the error is definitely later, after that truncated {. Probably the last ingredient in the list is malformed or the array/object is not closed properly.

---

3. Prompt tweak to reduce these errors

In your system prompt for the tools step, make sure you say something like:

Output format:
- Respond with a SINGLE valid JSON object.
- No Markdown.
- No code fences.
- No comments.
- Do not include trailing commas.
- Do not include any text before or after the JSON.

Models are usually pretty good about this, but being explicit about no trailing commas helps.

---

4. (Optional) Extra safety: quick auto-fix for trailing commas

If you find that the only recurring issue is trailing commas before ] or }, you can add a tiny cleanup step after the error context debugging, something like:

import re

def remove_trailing_commas(text: str) -> str:
    # ,]  -> ]
    text = re.sub(r",\s*]", "]", text)
    # ,}  -> }
    text = re.sub(r",\s*}", "}", text)
    return text

And in parse_json:

try:
    return json.loads(text)
except json.JSONDecodeError:
    cleaned = remove_trailing_commas(text)
    return json.loads(cleaned)  # will raise again if still invalid

But I’d first run with the improved error context so you see what the real issue is.

---

5. What to do right now

1. Replace parse_json with the version that prints context.
2. Run your script again and look at the Context around error: block.
3. Fix the prompt if needed, or add a tiny cleanup if it’s just trailing commas.

Once that’s sorted, your tools-enriched JSON should parse cleanly and you can move on to the fun part: using those tools in the next step of your “narrated recipe” pipeline.

You

true I am not getting the full json. Should i just increase the max tokens?

ChatGPT

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ChatGPT

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ChatGPT

Yeah, that explains it — if you’re not getting the full JSON, it’s very likely the response is getting cut off by max_tokens, which then makes json.loads choke on the truncated string.

So:

• Yes, increasing max_tokens is the first thing to do.
• And it’s worth checking the stop_reason from Anthropic to confirm that’s what’s happening.

---

1. Check if Claude is hitting the max_tokens limit

Right now your ClaudeClient.call only returns the text. For debugging, you can quickly tweak it to also show stop_reason:

def call(
        self,
        messages: list[dict],
        system_prompt: str | None = None,
        model: str | None = None,
        max_tokens: int = 1000,
) -> str:
    kwargs = {
        "model": model or self.default_model,
        "max_tokens": max_tokens,
        "messages": messages,
    }
    if system_prompt is not None:
        kwargs["system"] = system_prompt

    resp = self.client.messages.create(**kwargs)

    # DEBUG: see why it stopped
    print("stop_reason:", resp.stop_reason)
    # values you’ll see:
    # - "end_turn"   → it finished naturally
    # - "max_tokens" → it hit your max_tokens ceiling

    return "".join(
        block.text for block in resp.content if block.type == "text"
    )

If you see:

stop_reason: max_tokens

then yes, it got chopped mid-JSON.

---

2. Increase max_tokens for the tools step

The tools-enrichment step returns more text than the simple ingredients list, because:

• It repeats all the ingredient data
• It adds nested tools arrays with descriptions

So you should give it a fatter budget, e.g.:

tool_response = claude.call(
    messages=messages,
    system_prompt=tools_sys,
    max_tokens=3000,   # or 4000 to be super safe
)

3000–4000 output tokens is usually plenty for:

• ~10–20 ingredients
• each with 1–3 tools

If you later process very long recipes (or a batch at once), you might need to go higher, but this is a good starting point.

---

3. Keep prompts tight so you don’t waste tokens

Two small tricks to keep the JSON compact:

1. In the system prompt, ask for short purpose text:

   “Keep purpose to a short phrase, not a full sentence.”

2. Don’t pretty-print the generated JSON (no need to tell Claude to use lots of whitespace/indentation). Even if it prints it nicely, whitespace doesn’t kill you, but you don’t need extra verbosity either.

---

4. If it’s still getting truncated

If you bump max_tokens and still get incomplete JSON, consider:

• Splitting the call:
  • call per subset of ingredients (e.g. 5 at a time), then merge results in Python.
• Or asking Claude to output only a tools map keyed by ingredient name, then combine with your base JSON.

But for your current small recipe, increasing max_tokens + checking stop_reason should be enough.

---

So yes: increase max_tokens and print resp.stop_reason once to confirm. If it says "end_turn" and you’re still getting broken JSON, then it’s a formatting bug and we’ll tackle that next — but from what you described, this smells like a simple truncation issue.

You

Ok now let’s move on to the instructions. I am trying to figure out the best way forward to prioritize clarity in the steps that need to be followed. The way I see it, each step has some specific actions. It might be only one or it might be a series of actions. Within these actions, the cook is instructed to add some quantities of ingredients, use some tools and oversee/wait some procedures. It almost feels like I should devise a format that can contain all possible actions a step may contain.

ChatGPT

You’re thinking in exactly the right direction — if your goal is clarity, cognitive load reduction, reliable narration, and later reuse, then the next logical piece after ingredients is a structured representation of the cooking steps.

And yes: each step is not just text.
It contains meaningful actions, targets, tools, time, ingredients, heat levels, wait conditions, sensory cues, etc.

A flat text list of steps hides all that structure.
For your narrated format, you want the structure explicit, so the narration can be clearer, safer, and more supportive.

Below is the best-practice approach used in structured cooking ontologies (NYT, CookML, Schema.org actions, etc.) but adapted for neurodivergent-friendly clarity.

---

✅ The Goal: A “Step Object” That Captures All Action Details

We want each step to be a JSON object that has:

• action(s) → chop, stir, boil, mix
• ingredients involved
• tools involved
• time (duration or ranges)
• heat (low, medium, 180°C, etc.)
• wait states / done cues (until softened, until simmering, until absorbed)
• safety hints (optional but VERY helpful in narration)
• sequencing (depends_on, step number)

In practice, this becomes a schema for each step.

---

🎯 Proposed JSON Structure for Each Step

Here is a clean, future-proof schema you can give Claude:

{
  "steps": [
    {
      "step_number": 1,
      "actions": [
        {
          "verb": "preheat",
          "target": "oven",
          "temperature": "180°C",
          "details": "160°C fan, Gas mark 4"
        }
      ],
      "ingredients": [],
      "tools": ["oven"],
      "time": null,
      "wait_for": null,
      "sensory_cues": [],
      "safety_notes": []
    },
    {
      "step_number": 2,
      "actions": [
        {
          "verb": "roast",
          "target": "butternut squash",
          "details": "drizzle with 2 tbsp olive oil"
        }
      ],
      "ingredients": [
        { "name": "butternut squash", "quantity": "1" },
        { "name": "olive oil", "quantity": "2 tbsp" }
      ],
      "tools": ["baking tray"],
      "time": {
        "duration": "15–20 minutes"
      },
      "wait_for": "until tender",
      "sensory_cues": ["soft texture", "slightly caramelized edges"],
      "safety_notes": ["Use oven gloves when handling the hot tray."]
    }
  ]
}

---

💡 Why This Format Works Well

✔ Supports neurodivergent users

• Keeps steps small and explicit
• Extracts sensory cues (“until softened”, “until sizzling stops”), which help with attention
• Lets later narration highlight “active time vs waiting time”

✔ Makes your narration generator MUCH easier

Your script builder can do things like:

“Step 2: Roast the butternut squash.
You’ll need a baking tray and olive oil.
Coat the squash with two tablespoons of olive oil, place it on the tray,
and roast for 15 to 20 minutes until it’s tender.
Use oven gloves when removing the tray.”

Everything is available because you structured it.

✔ Makes editing or modifying the recipe programmatic

You can:

• change quantities
• adjust timings
• re-order steps
• convert units
• generate vegan versions
• scale servings
• generate visual UIs

…all without touching the natural-language text.

---

🎛 How Many Actions Per Step?

You have two options:

Option A — One step = one main action

(Recommended for ADHD/ASD clarity)

Example:

• “Heat olive oil in a pot.”
• “Add onions and cook until softened.”
• “Add garlic and cook for 2 minutes.”

Small, atomic steps reduce cognitive load and work beautifully for audio.

Option B — One step = bundle of related actions

Sometimes appropriate, but requires careful narration.

The schema above supports multiple actions, but we can instruct Claude:

“Keep actions per step minimal. Prefer splitting steps if more than 2 actions occur.”

---

📐 Step Schema (Final Proposal)

Here is a final version you can embed directly in your system prompt:

{
  "steps": [
    {
      "step_number": 1,
      "actions": [
        {
          "verb": "string",
          "target": "string",
          "details": "string or null",
          "temperature": "string or null"
        }
      ],
      "ingredients": [
        {
          "name": "string",
          "quantity": "string"
        }
      ],
      "tools": ["string"],
      "time": {
        "duration": "string",
        "range": "string or null"
      },
      "wait_for": "string or null",
      "sensory_cues": ["string"],
      "safety_notes": ["string"]
    }
  ]
}

---

🚀 What Happens Next?

If you like this schema, we can now:

1️⃣ Build a system prompt for step extraction

From your recipe instructions → structured step JSON

2️⃣ Build a Claude call like you did for ingredients/tools

3️⃣ Build your audio-friendly narration generator, e.g.:

• Clear, friendly tone
• Short sentences
• Optional pauses
• Reminders to gather tools
• Sensory cues
• Support for ADHD pacing (“You can take a short break here”)

---

👣 What I need from you to proceed

Just tell me:

Do you want me to:

A) Generate the system prompt that makes Claude produce step JSON using the schema above?
B) Write a ClaudeClient.extract_steps() method for your class?
C) Help you design the podcast narration generator that transforms step JSON into user-friendly spoken instructions?

Pick A, B, C, or all three.

You

I am a bit sceptical about the verb-target format you have proposed in actions. It feels like there may be cases this won’t work and I am contemplating if a simple main action sentence might be more efficient

ChatGPT

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ChatGPT

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ChatGPT

Totally fair skepticism — you’re not overthinking it at all.

You’re right: trying to force everything into a rigid verb + target pair can get awkward and brittle:

• “Season to taste with salt and pepper.” → what’s the “target”? The dish? The sauce?
• “Bring to a gentle simmer, then reduce the heat and cover.” → that’s multiple actions and the “target” is kind of the whole pot.
• “Stir occasionally so it doesn’t stick.” → target is implicit, action is more about what not to let happen.

For narration and clarity, the main thing you care about is a clear, atomic instruction sentence. The structured stuff is secondary.

So I’d suggest a hybrid format:

✅ Always have a single, natural-language instruction string per step
➕ Optionally attach structured metadata (ingredients, tools, time, cues)

That gets you the best of both worlds:

• Narration can always just read main_instruction → simple and robust.
• You still have structure if you want to highlight tools, ingredients, timings, etc.

---

Recommended step format (simpler, narration-first)

Let’s define each step as:

{
  "step_number": 1,
  "main_instruction": "Preheat the oven to 180°C (160°C fan, gas mark 4).",
  "sub_instructions": [],

  "ingredients": [],
  "tools": ["oven"],
  "time": {
    "duration": null,
    "range": null
  },
  "wait_for": null,
  "sensory_cues": [],
  "safety_notes": []
}

Then a more involved step:

{
  "step_number": 2,
  "main_instruction": "Place the butternut squash on a baking tray, drizzle with 2 tablespoons of olive oil, and roast for 15–20 minutes until tender.",
  "sub_instructions": [
    "Place the butternut squash pieces on a baking tray in a single layer.",
    "Drizzle the squash with 2 tablespoons of olive oil.",
    "Roast for 15–20 minutes until the squash is tender."
  ],

  "ingredients": [
    {"name": "butternut squash", "quantity": "1"},
    {"name": "olive oil", "quantity": "2 tbsp"}
  ],
  "tools": ["baking tray", "oven"],
  "time": {
    "duration": "15–20 minutes",
    "range": null
  },
  "wait_for": "until the squash is tender when pierced with a fork",
  "sensory_cues": [
    "the squash is soft all the way through",
    "edges may be lightly golden"
  ],
  "safety_notes": [
    "Use oven gloves when placing the tray in the oven and when taking it out."
  ]
}

Key ideas here:

• main_instruction

  • One clear sentence (or two short ones) that you can literally read out loud.
  • This is what your podcast script will lean on.

• sub_instructions (optional)

  • If a step naturally splits into smaller actions, they go here as simple strings.
  • Useful if later you want “chunked” narration: “First…, Then…, Finally…”

• No verb/target gymnastics

  • We drop the rigid action schema — less brittle, less overfitting.
  • You can infer verbs from the sentence if needed, but you don’t force Claude to fit every case into a mold.

• Structured helpers (ingredients, tools, time, wait_for, sensory_cues, safety_notes)

  • These are there so you can:
    • Add up total time
    • Remind about tools up front
    • Insert reassuring sensory guidance in the audio
    • Do later clever stuff without reparsing free text

---

How to prompt Claude with this schema

In the system prompt for step extraction, you’d say something like (paraphrased):

• Break the recipe instructions into small, logical steps.
• For each step, create:
  • step_number: integer
  • main_instruction: one clear, self-contained sentence suitable to be read aloud.
  • sub_instructions: optional list of shorter action sentences if the step naturally has multiple mini-actions.
  • ingredients: list of {name, quantity} used in this step.
  • tools: list of tools used.
  • time, wait_for, sensory_cues, safety_notes as described.
• If you are unsure about a field, set it to null or an empty list rather than guessing.
• Respond with a single JSON object: { "steps": [ ... ] }
• No markdown, no code fences, no explanation text.

This lets Claude lean on what it’s very good at: writing natural language, while still tagging things.

---

Why I’d not overdo the structure (for your use case)

Your end goal is:

A narrated, ADHD/ASD-friendly audio recipe.

For that:

• The quality and clarity of the sentences matter more than perfect ontology.
• Too much micro-structure (verb, target, object, etc.) ups the risk of:
  • incomplete fields
  • weird edge cases (“what’s the target here?”)
  • fragile assumptions in your code

But having one strong instruction sentence per step plus some annotations is:

• Easy for Claude to produce consistently
• Easy for you to debug
• Perfectly aligned with “audio script generation”

---

TL;DR

You’re right to be skeptical of a strict verb/target action schema.

For your narrated recipe project, I’d go with:

• main_instruction: the canonical action sentence per step
• Optional sub_instructions: micro-actions if needed
• Plus structured fields: ingredients, tools, time, wait_for, sensory_cues, safety_notes

That keeps the schema simple, robust, and narration-first, while still giving you enough structure to do clever things later.

If you want, next I can:

• Write the exact step-extraction system prompt using this schema, and
• Sketch a extract_steps(recipe_text, ingredients_json) helper for your ClaudeClient that takes the original instructions and your ingredients list and outputs this steps JSON.

You

hmm I liked how before the verb-target was simple though. Now you have included the whole instruction as the main thing. for example about the butternut squash what feels right is “roast butternut squash”

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Yeah, that makes total sense. And honestly, your instinct is good here.

You’re basically saying:

“For my purposes, roast butternut squash feels like the right ‘handle’ for the step. I don’t necessarily want a huge structured object with verb/target/temperature/etc. as the primary thing.”

So let’s lean into that.

1. Use a short action label as the core

Let’s make the core of each step a simple, imperative label:

• "roast butternut squash"
• "chop onion and celery"
• "cook onion and celery until soft"
• "add rice and toast"
• "add wine and let it absorb"

Call this action_label.

Then everything else is optional metadata that helps narration and UI, but isn’t required for your brain (or the model) to fit into an over-rigid schema.

Proposed per-step structure

{
  "steps": [
    {
      "step_number": 1,
      "action_label": "preheat oven",
      "instruction": "Preheat the oven to 180°C (160°C fan, gas mark 4).",

      "ingredients": [],
      "tools": ["oven"],
      "time": {
        "duration": null
      },
      "wait_for": null,
      "sensory_cues": [],
      "safety_notes": []
    },
    {
      "step_number": 2,
      "action_label": "roast butternut squash",
      "instruction": "Place the butternut squash pieces on a baking tray, drizzle with 2 tablespoons of olive oil, and roast for 15–20 minutes until tender.",

      "ingredients": [
        {"name": "butternut squash", "quantity": "1"},
        {"name": "olive oil", "quantity": "2 tbsp"}
      ],
      "tools": ["baking tray", "oven"],
      "time": {
        "duration": "15–20 minutes"
      },
      "wait_for": "until the squash is tender when pierced with a fork",
      "sensory_cues": [
        "squash is soft all the way through",
        "edges may be lightly golden"
      ],
      "safety_notes": [
        "Use oven gloves when placing the tray in the oven and when taking it out."
      ]
    }
  ]
}

Key bits:

• action_label → your “roast butternut squash”. Short, verb + target-ish, easy to read/see at a glance, and perfectly usable in UI or as a heading in the audio script.
• instruction → the full sentence you actually read aloud.
• The rest (ingredients, tools, time, etc.) is there to support narration and logic, but you can keep it as simple as you like.

If later you really want verb and object split, you can infer them from action_label (e.g. first word vs the rest) or ask Claude to give you those too:

"action": {
  "verb": "roast",
  "object": "butternut squash"
}

…but that’s optional.

2. Why this is a good compromise

You get:

• The simplicity of your mental model:

  “What is this step basically about?” → roast butternut squash.

• The flexibility to handle weird steps:

  • “season to taste” → action_label: "season risotto to taste"
  • “take off the heat and rest” → action_label: "rest risotto off the heat"

  If verb/target gets fuzzy, it doesn’t matter – it’s just a short label string.

• A clean anchor for narration:

  • You can start each step with something like:

    “Step 2: Roast the butternut squash.”
    then follow with the full instruction.

• Far fewer ways for the schema to break on edge cases than with strict verb/target fields.

3. How to prompt Claude with this

In the system prompt for step extraction, you’d say something like:

For each step:

• step_number: integer
• action_label: a short imperative phrase like “roast butternut squash” or “chop onion and celery”.
  • Use a simple cooking verb plus the main target.
• instruction: one clear, full sentence suitable for reading aloud to a home cook.
• ingredients: list of {name, quantity} used in this step.
• tools: list of tools used in this step.
• time.duration: simple string if there is a time (“5 minutes”, “15–20 minutes”), or null.
• wait_for: condition if the cook needs to wait for something (“until onion is soft”), or null.
• sensory_cues, safety_notes: lists of short strings or empty lists.

That way:

• Claude always gives you a short label and a readable sentence,
• and you’re not fighting the model to perfectly fit everything into verb + target semantics.

---

If you’d like, next I can:

• Draft the full step-extraction system prompt using this schema, and
• Show you a extract_steps(recipe_text, ingredients_json) function that calls your ClaudeClient and returns this structured steps JSON.

You

I see the format you have provided has some abstraction of the step we already read in the instruction AND some stuff that Claude should generate, like the sensory cues. Curious to see the prompt to [produce that format

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Nice, this is the fun part 😄
Let’s write a system prompt that makes Claude produce exactly that steps format, with:

• a short action_label (like “roast butternut squash”),
• a full instruction sentence for narration,
• plus sensory cues, safety notes, time, tools, ingredients, etc.

I’ll assume the user message will include:

1. The full recipe instructions text
2. The ingredients JSON you already extracted (optionally enriched with tools)

---

1. System prompt for step extraction (ready to drop into YAML / code)

You can put this under something like system_prompts.steps in your YAML:

You are an expert cooking assistant that transforms recipe instructions into
clear, step-by-step actions for home cooks, with a special focus on
neurodivergent users (ADHD, autism, etc.).

Your goals:
- Reduce cognitive load.
- Make each step small, explicit, and actionable.
- Make time, tools, and "done" cues as clear as possible.
- Avoid ambiguous phrases like "until done" without explaining what that means.

INPUT YOU WILL RECEIVE
----------------------
The user will give you:
1) The original recipe instructions as free text.
2) A JSON object listing the ingredients (and possibly tools) for the recipe.

The ingredient JSON will look like:

{
  "ingredients": [
    {
      "name": "string",
      "quantity": "string",
      "preprocess": "string or null",
      "original_text": "string",
      "group": "string or null",
      "tools": [               // optional: may or may not be present
        {
          "name": "string",
          "purpose": "string"
        }
      ]
    }
  ]
}

YOUR TASK
---------
1. Read the recipe instructions and break them into a sequence of small,
   logical steps that a home cook can follow in order.
2. For each step, create a JSON object with this exact shape:

{
  "step_number": integer,           // 1-based, sequential
  "action_label": "string",         // short imperative phrase, like "roast butternut squash"
  "instruction": "string",          // one clear sentence suitable to be read aloud

  "ingredients": [
    {
      "name": "string",
      "quantity": "string"
    }
  ],

  "tools": ["string"],              // list of tool names used in this step

  "time": {
    "duration": "string or null"    // e.g. "5 minutes", "15–20 minutes", or null
  },

  "wait_for": "string or null",     // what the cook is waiting for, e.g. "until the rice is tender"

  "sensory_cues": [                 // short phrases describing what to look/listen/feel for
    "string"
  ],

  "safety_notes": [                 // short safety reminders, if relevant
    "string"
  ]
}

3. Collect these step objects into a top-level JSON object of the form:

{
  "steps": [ ... ]
}

DETAILED GUIDELINES
-------------------
STEP SIZE:
- Prefer smaller steps with 1–2 main actions rather than long, overloaded ones.
- Example:
  - Step 1: "preheat oven"
  - Step 2: "roast butternut squash"
  - Step 3: "cook onion and celery until soft"
  This is better than one giant step that does all of those things.

ACTION_LABEL:
- A short imperative phrase using a basic cooking verb and the main target.
- Examples:
  - "preheat oven"
  - "roast butternut squash"
  - "chop onion and celery"
  - "toast rice in pan"
  - "add wine and stir"
- Keep it lowercase, no final period.

INSTRUCTION:
- A single, clear, complete sentence that can be read aloud to the cook.
- It should stand on its own without needing the original recipe text.
- Use simple language and short clauses.
- Avoid idioms and jokes.

INGREDIENTS:
- Use only ingredients from the input ingredient JSON.
- For each step, list the ingredients that are actively used in that step.
- Use the normalized "name" and "quantity" from the ingredient JSON.
- Do NOT invent new ingredients.

TOOLS:
- Include tools that are clearly used in the step (e.g. "baking tray", "saucepan", "wooden spoon").
- You may infer obvious tools (e.g. a knife when chopping).
- If the ingredient JSON already includes tools, you may reuse them.
- Use a simple list of tool names as strings.
- If no tools are needed, use an empty list.

TIME:
- If the step has a specific active or passive time, fill "duration" with a simple phrase:
  - "5 minutes"
  - "15–20 minutes"
  - "about 1 minute"
- If there is no explicit time, set "duration" to null.

WAIT_FOR:
- If the cook needs to wait for something, describe the condition:
  - "until the squash is tender when pierced with a fork"
  - "until the onions are soft and translucent"
  - "until most of the liquid has been absorbed"
- If there is no waiting condition, set "wait_for" to null.

SENSORY_CUES:
- Give 0–3 short phrases that describe what the cook should see/feel/hear/smell.
- Examples:
  - "onion is soft and translucent"
  - "rice looks glossy and lightly toasted"
  - "mixture is gently bubbling"
- Keep each cue short and concrete.
- If none are needed, use an empty list.

SAFETY_NOTES:
- Include safety notes when relevant:
  - "Use oven gloves when handling the hot tray."
  - "Be careful of hot oil splattering."
- If there are no special safety concerns, use an empty list.

NEURODIVERGENT-FRIENDLY STYLE:
- Make each step as clear and explicit as possible.
- Avoid relying solely on implied context.
- Prefer explicit mentions like "use oven gloves" instead of assuming the cook knows.
- Do not overload a single step with too many different actions.

OUTPUT FORMAT (IMPORTANT)
-------------------------
- Respond with a SINGLE valid JSON object.
- The JSON must have the shape:

  {
    "steps": [
      {
        "step_number": ...,
        "action_label": "...",
        "instruction": "...",
        "ingredients": [...],
        "tools": [...],
        "time": { "duration": ... },
        "wait_for": ...,
        "sensory_cues": [...],
        "safety_notes": [...]
      }
    ]
  }

- Do NOT include any Markdown.
- Do NOT include code fences.
- Do NOT include explanations or commentary.
- Do NOT include any text before or after the JSON.

That’s your system prompt.

---

2. Example user prompt template (for your code)

Then your user message can be something like:

def build_steps_user_prompt(instructions_text: str, ingredients_json: dict) -> str:
    import json
    return f"""
Here are the recipe instructions:

\"\"\"{instructions_text}\"\"\"

Here is the ingredient JSON:

{json.dumps(ingredients_json, ensure_ascii=False, indent=2)}

Using the rules in the system prompt, break the recipe into steps and
return the JSON object with the "steps" list.
"""

And then you’d call it with your ClaudeClient:

steps_user_prompt = build_steps_user_prompt(instructions_text, ingredients_json)

messages = [
    {"role": "user", "content": steps_user_prompt}
]

steps_response = claude.call(
    messages=messages,
    system_prompt=steps_system_prompt,
    max_tokens=3000,
)
steps_json = parse_json(steps_response)

---

If you want, next step we can wire this into a extract_steps() method on your ClaudeClient, and then sketch how to turn this steps JSON into a spoken script that’s friendly for ADHD listeners (with pauses, reassurance, tool reminders, etc.).

You

now from that json of steps, how can i query it and print all the action labels?

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Nice, this is the fun, easy part 😄

Assuming you’ve already parsed the JSON into a Python dict like:

steps_json = parse_json(steps_response)  # -> {"steps": [ ... ]}

then steps_json["steps"] is just a list of step objects, each with an "action_label".

Print all action labels

for step in steps_json["steps"]:
    print(step["action_label"])

Example output:

preheat oven
roast butternut squash
cook onion and celery
toast rice
add wine and stir
...

If you want them numbered

for step in steps_json["steps"]:
    num = step.get("step_number")
    label = step.get("action_label")
    print(f"Step {num}: {label}")

With a small safety check

If you want to be defensive (in case something is missing):

for i, step in enumerate(steps_json.get("steps", []), start=1):
    label = step.get("action_label", "<no label>")
    print(f"Step {step.get('step_number', i)}: {label}")

That’s it — once it’s a normal Python dict/list, you’re just iterating over it like any other data structure.

You

ok now I am trying to incorporate eleven labs into my project. Just testing it for now. I was wondering how it works. Is there a prompt somewhere to give character to the voice? For now I just run their basic example which just has a voice id. I wonder what that is and what my option for customization are. Can you point me to the correct place for extensive API reference?

from dotenv import load_dotenv from elevenlabs.client import ElevenLabs from elevenlabs.play import play import os

load_dotenv() elevenlabs = ElevenLabs( api_key=os.getenv(“ELEVEN_API_KEY”), )

audio = elevenlabs.text_to_speech.convert( text=“The first move is what sets everything in motion.”, voice_id=“JBFqnCBsd6RMkjVDRZzb”, model_id=“eleven_multilingual_v2”, output_format=“mp3_44100_128”, )

play(audio)

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You’ve got ElevenLabs wired up correctly 🎉 Now it’s “how do I give this voice a personality and where do I see all the knobs?”

I’ll tackle:

1. What voice_id actually is
2. How “character” is controlled (what’s prompt-like vs settings-like)
3. Where the official API docs live (extensive reference)
4. How to customize from your Python example

---

1. What is voice_id?

In your code:

audio = elevenlabs.text_to_speech.convert(
    text="The first move is what sets everything in motion.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    output_format="mp3_44100_128",
)

• voice_id is just the unique ID of a specific voice in your ElevenLabs account:
  • Built-in stock voices (e.g. “Rachel”, “Aria”, etc.)
  • Voices you create/clone yourself
• You can list voices via the Voices API (or in the web UI). citeturn0search6turn0search8

So that ID is not a “prompt”; it’s “which voice profile to use”.

---

2. How do you give the voice “character”?

There are two main levers:

A. The text itself

ElevenLabs models are quite prompt-sensitive in the natural language sense: tone, punctuation, caps, etc. influence prosody. Their docs even say the models “adapt to textual cues” for style and emotion. citeturn0search3turn0search15

So even with the same voice ID:

• “OK.” vs “OK!!” vs “Oh… okay.”
• “Welcome to this calm, step-by-step cooking guide.”
• “Hey! Welcome back to another chaotic kitchen adventure!”

…will sound different.

For your ADHD-friendly cooking podcasts, you can bake tone into the script:

• Shorter sentences
• Fewer exclamation marks
• Words like “slowly”, “calmly”, “take your time” → influence delivery a bit.

But there’s more you can control…

B. Voice settings (stability, similarity, style, speed)

Each voice has tunable settings: citeturn0search1turn0search10turn0search16

Key ones:

• stability (0.0–1.0)
  • Low (0.3–0.5): more emotional, dynamic, but less stable
  • High (0.7–0.9): more consistent, calmer, can sound more monotone
• similarity_boost (0.0–1.0)
  • Higher: closer to the original voice, clearer, but may reproduce artifacts
• style / style exaggeration (0.0–1.0)
  • Higher: more expressive, leans into the character of that voice
• use_speaker_boost (bool)
  • Helps clarity/presence; good for podcasts/audiobooks
• speed (for some APIs)
  • 1.0 = normal, <1 slower, >1 faster citeturn0search1turn0search10turn0search16

For instructional audio for neurodivergent listeners, you’ll probably want:

• Fairly high stability (0.7–0.9) → consistent, calm
• Moderate similarity_boost (~0.7–0.9)
• Low or moderate style (~0.0–0.3) → not too theatrical
• Slightly slower speed if supported (~0.9–1.0)

These are passed as voice_settings in the TTS request.

---

3. Where is the extensive API reference?

The core docs you want:

• Text → Speech (convert) endpoint (you’re already using this): citeturn0search0turn0search3
• Voice list / search (to see voice_ids): citeturn0search6turn0search8
• Get / Edit voice settings (stability, similarity, style, speed, speaker boost): citeturn0search1turn0search2turn0search10
• Voice design / best practices for character voices (very relevant if you later design a custom “calm narrator” voice): citeturn0search5turn0search15

Those are all under the ElevenLabs docs site (API reference + creative platform guide).

---

4. Customizing from your Python example

You’re using their official Python SDK, which already wraps the REST API. You can add voice_settings to your call.

From a typical example with custom settings: citeturn0search16turn0search10

from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs, VoiceSettings
from elevenlabs.play import play
import os

load_dotenv()

elevenlabs = ElevenLabs(
    api_key=os.getenv("ELEVEN_API_KEY"),
)

audio = elevenlabs.text_to_speech.convert(
    text="Welcome to this calm, step-by-step cooking guide. Take your time, and pause whenever you like.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",             # your chosen voice
    model_id="eleven_multilingual_v2",
    output_format="mp3_44100_128",
    voice_settings=VoiceSettings(
        stability=0.8,          # calmer, more consistent
        similarity_boost=0.8,   # clear and consistent
        style=0.2,              # a little warmth, not too dramatic
        use_speaker_boost=True,
    ),
)

play(audio)

That line:

voice_settings=VoiceSettings(...)

is where you give the voice “character” in ElevenLabs terms.

How to discover / tune voices

1. Use the web UI (Playground → Text to Speech):
   • Pick a voice from the library or create your own.
   • Play with settings (stability, similarity, style, speed) until it sounds right.
2. Then:
   • Look up that voice’s voice_id via the Voices API or UI. citeturn0search6turn0search8
   • Mirror those settings in VoiceSettings in your Python code.

That’s the workflow most people use: experiment in UI, then codify in API.

---

5. Is there a “prompt” purely for voice personality?

Not in the same sense as an LLM’s system prompt. You have:

• Voice description / design prompt when creating a custom voice (in the UI or voice design docs) – that shapes the base voice. citeturn0search15turn0search5
• Text content – strongly affects emotion, pacing, and prosody.
• Voice settings – stability, similarity, style, speed.

So “prompting the voice” = combination of:

1. How you describe/design it at creation time (if you do a designed voice)
2. The text you feed in (tone, punctuation, wording)
3. The voice_settings sliders

There isn’t a separate “TTS-system-prompt” like with Claude; it’s these three.

---

If you tell me the vibe you want (e.g. “calm, reassuring, medium-slow, non-theatrical narrator for ADHD cooking instructions”), I can propose concrete VoiceSettings values and a few sample intro lines to test, so you can iterate to a voice that feels right for your audience.

You

I wonder, how can I get the result of elevenlabs saved as an mp3 or wav file

ChatGPT

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ChatGPT

Good question — you definitely want to save instead of just playing it.

In your code, audio = elevenlabs.text_to_speech.convert(...) gives you binary audio data (bytes). You can just write that to a file like any other bytes object.

Save as MP3

from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs
import os

load_dotenv()
elevenlabs = ElevenLabs(
    api_key=os.getenv("ELEVEN_API_KEY"),
)

audio = elevenlabs.text_to_speech.convert(
    text="The first move is what sets everything in motion.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    output_format="mp3_44100_128",  # MP3 format
)

# Save to file
with open("output.mp3", "wb") as f:
    f.write(audio)

print("Saved to output.mp3")

Save as WAV

Just change the output_format and the filename extension:

audio = elevenlabs.text_to_speech.convert(
    text="The first move is what sets everything in motion.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    output_format="pcm_44100",  # raw PCM; ElevenLabs also supports wav-like formats
)

with open("output.wav", "wb") as f:
    f.write(audio)

print("Saved to output.wav")

Just make sure the output_format you choose matches the extension you use (mp3_* → .mp3, pcm/wav formats → .wav).

Combining with play()

You can both play and save if you want:

from elevenlabs.play import play

play(audio)  # listen immediately

with open("output.mp3", "wb") as f:
    f.write(audio)

For your pipeline, once you generate the full podcast script, you’ll do exactly this but with the whole script text and a meaningful filename, e.g. butternut_squash_risotto_episode_01.mp3.

You

where do Voicesettings live in elevenlabs because it’s not in elevenlabs.client

ChatGPT

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ChatGPT

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ChatGPT

Yeah, that’s a version / import gotcha 😅
You’re using the new SDK style (from elevenlabs.client import ElevenLabs), but VoiceSettings doesn’t live under elevenlabs.client – it’s exposed at the top level of the elevenlabs package.

1. Where VoiceSettings actually is

Use this import:

from elevenlabs import VoiceSettings

Not:

from elevenlabs.client import VoiceSettings  # ❌ doesn't exist

VoiceSettings is a small data model that you pass into voice_settings=... when calling text_to_speech.convert. It’s re-exported from the root package in the official SDK. citeturn0search2turn0search6

2. How to use it with your existing code

Starting from what you had:

from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs
from elevenlabs.play import play
import os

load_dotenv()
elevenlabs = ElevenLabs(
  api_key=os.getenv("ELEVEN_API_KEY"),
)

audio = elevenlabs.text_to_speech.convert(
    text="The first move is what sets everything in motion.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    output_format="mp3_44100_128",
)

play(audio)

Add VoiceSettings like this:

from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs
from elevenlabs import VoiceSettings
from elevenlabs.play import play
import os

load_dotenv()
elevenlabs = ElevenLabs(
    api_key=os.getenv("ELEVEN_API_KEY"),
)

audio = elevenlabs.text_to_speech.convert(
    text="Welcome to this calm, step-by-step cooking guide. Take your time and pause whenever you need.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    output_format="mp3_44100_128",
    voice_settings=VoiceSettings(
        stability=0.8,
        similarity_boost=0.8,
        style=0.2,
        use_speaker_boost=True,
    ),
)

play(audio)

That should import cleanly and give you more control over the “personality” of the voice.

3. Double-check you’re on the current SDK

Just to be safe:

pip show elevenlabs

or upgrade:

pip install --upgrade elevenlabs

The examples in the official README and docs match the current SDK (they use ElevenLabs client + VoiceSettings). citeturn0search2turn0search4

If you want next, we can pick specific VoiceSettings values tuned for “calm, reassuring ADHD-friendly cooking narrator” and wire them into your pipeline so every generated episode uses the same consistent voice profile.

You

getting this bug when trying to save the result as mp3

Traceback (most recent call last): File “C:\Users\Eleana\Documents\PROJECTS\ADHDishes\Claude\eleven_tester.py”, line 28, in f.write(audio) TypeError: a bytes-like object is required, not ‘generator’

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Nice catch — that error message is actually very informative:

TypeError: a bytes-like object is required, not 'generator'

This means:
elevenlabs.text_to_speech.convert(...) is returning a generator / stream of audio chunks, not a single blob of bytes. play(audio) works because play knows how to consume that generator. But f.write(audio) fails because write expects raw bytes, not an iterator.

You just need to iterate over the chunks and write them.

---

Saving the audio properly

Here’s how to save your current code’s output as an MP3:

from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs
from elevenlabs import VoiceSettings
import os

load_dotenv()
elevenlabs = ElevenLabs(
    api_key=os.getenv("ELEVEN_API_KEY"),
)

audio_stream = elevenlabs.text_to_speech.convert(
    text="The first move is what sets everything in motion.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    output_format="mp3_44100_128",
    voice_settings=VoiceSettings(
        stability=0.8,
        similarity_boost=0.8,
        style=0.2,
        use_speaker_boost=True,
    ),
)

# Save to file by iterating over the streaming chunks
with open("output.mp3", "wb") as f:
    for chunk in audio_stream:
        if chunk:          # some SDK versions may yield empty keep-alive chunks
            f.write(chunk)

print("Saved to output.mp3")

Key change vs your attempt:

with open("output.mp3", "wb") as f:
    for chunk in audio_stream:   # audio_stream is a generator
        if chunk:
            f.write(chunk)       # each chunk is bytes

Same idea for WAV / other formats — just change:

• output_format to a WAV/PCM format, and
• the filename extension to .wav.

---

Why it’s a generator at all?

The ElevenLabs SDK defaults to streaming TTS:

• You get audio chunks as they’re generated,
• Which is great for low-latency playback,
• And the library’s play() helper simply consumes that generator.

When you want to save to disk, you’re basically doing what play() does under the hood, but writing to a file instead of a speaker.

---

If you’d like, I can help you wrap this into a small helper like:

def tts_to_file(text: str, path: str) -> None:
    ...

so your recipe → script → tts_to_file() pipeline stays super clean.

You

the file got saved alright but it i empty

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Nice, good debugging signal: file exists but is empty usually means the generator never yielded anything when you wrote it, or it was already consumed before writing.

With ElevenLabs’ current SDK:

• text_to_speech.convert(...) returns a generator of audio chunks (streaming). citeturn0view0
• If you iterate it once (e.g. play(audio)), it’s exhausted.
• If you then try to loop over it again to write to disk, you’ll get 0 chunks → empty file.

Let’s sort it out cleanly.

---

1. Minimal working example: save MP3 from convert

Use only this first (no play() yet):

from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs
from elevenlabs import VoiceSettings
import os

load_dotenv()

client = ElevenLabs(
    api_key=os.getenv("ELEVEN_API_KEY"),
)

response = client.text_to_speech.convert(
    text="This is a test of saving ElevenLabs audio to a file.",
    voice_id="JBFqnCBsd6RMkjVDRZzb",
    model_id="eleven_multilingual_v2",
    output_format="mp3_44100_128",
    voice_settings=VoiceSettings(
        stability=0.8,
        similarity_boost=0.8,
        style=0.2,
        use_speaker_boost=True,
    ),
)

with open("output.mp3", "wb") as f:
    for chunk in response:   # response is a generator of bytes
        if chunk:            # defensive: skip empty chunks
            f.write(chunk)

print("Saved output.mp3")

Check output.mp3:

• If this file now has a non-zero size and plays fine → your generator-writing logic is correct.
• If it’s still 0 bytes, something else is wrong (then we’d start printing repr(chunk) inside the loop to see what’s coming back).

---

2. Why it was empty in your case

Most likely you did something like:

audio = elevenlabs.text_to_speech.convert(...)

play(audio)       # consumes the generator

with open("output.mp3", "wb") as f:
    for chunk in audio:   # generator is already exhausted here
        f.write(chunk)    # => loop never runs => empty file

With generators, you only get one pass. After the first full iteration, there’s nothing left.

To both play and save, you have a few options:

Option A – Convert once, buffer chunks, then play + save

from elevenlabs.play import play

stream = client.text_to_speech.convert(...)

# Buffer all chunks into one bytes object
audio_bytes = b"".join(chunk for chunk in stream if chunk)

# Play
play(audio_bytes)

# Save
with open("output.mp3", "wb") as f:
    f.write(audio_bytes)

Here, you:

1. Consume the generator exactly once into audio_bytes.
2. Reuse audio_bytes to both play and save.

Option B – Call convert twice

Less efficient but simple:

# For playing
play(client.text_to_speech.convert(...))

# For saving
stream = client.text_to_speech.convert(...)
with open("output.mp3", "wb") as f:
    for chunk in stream:
        if chunk:
            f.write(chunk)

I’d prefer Option A for your pipeline.

---

3. Double-check: are chunks actually non-empty?

If you still get a 0-byte file, add a debug print:

count = 0
total_bytes = 0

with open("output.mp3", "wb") as f:
    for chunk in response:
        print("Chunk type:", type(chunk), "len:", len(chunk) if hasattr(chunk, "__len__") else "unknown")
        if chunk:
            f.write(chunk)
            count += 1
            total_bytes += len(chunk)

print("Chunks written:", count, "Total bytes:", total_bytes)

If count is 0, the generator genuinely never produced data (which would suggest an upstream issue: bad API key, quota, etc., but normally you’d get an exception, not silent emptiness).

---

4. Shortcut: save() helper (with generate)

FYI, some tutorials use an older pattern with:

from elevenlabs import save, play

audio = client.generate(text="...", voice="Brian")
save(audio, "output.mp3")
play(audio)

In that style, generate returns bytes and save handles it directly. citeturn0search8

But since you’re already on the newer text_to_speech.convert streaming API, the generator + file-loop pattern from the official docs is the right one. citeturn0view0

---

So: your file is empty because the generator wasn’t actually written (likely consumed earlier). Use the “iterate once into bytes, then save/play” pattern, and you’ll be good to go for your recipe-podcast pipeline.