That sounds like a really creative and fun project! The challenge you’re facing—where a long prompt overwhelms the LLM and produces less coherent articles—is common. There are a few approaches you can take to refine the article generation process while still allowing the model to maintain agency:
Instead of dumping all the information into one long prompt, you can guide the LLM through a structured, iterative process using LangChain. Here’s a breakdown:
- Instead of giving a full article prompt, start by asking the model to generate its thoughts and observations on the given topic.
- This could be framed as the tree “listening to the wind,” “reading the rings of its ancestors,” or “observing the forest.”
- Output: A short bullet point list of insights or angles.
- Based on the insights from Step 1, ask the model:
“How would you structure an article based on these observations?”
- Output: A structured outline, ensuring a logical flow.
- Once the outline is set, break down the writing into sections (e.g., Introduction, Background, Analysis, Conclusion).
- Instead of generating the whole article at once, let the model write each section separately while keeping context.
- This reduces cognitive load and makes the writing more focused.
- Ask the model to critique its own work before finalizing.
- Example prompt:
“Read this draft as if you were an ancient tree journalist. What would you refine?”
- This keeps the LLM engaged in an autonomous feedback loop.
If you still want the model to retain some degree of agency without micromanaging it, consider using a CoT approach:
- Instead of giving direct instructions, encourage the model to “think aloud” by prompting:
“As a tree journalist, how would you approach writing an article on [topic]?”
- The LLM will generate its own plan, which you can then refine.
To make sure the tree journalist remembers its personality and past observations, you can:
- Store prior responses in a memory buffer using LangChain.
- When prompting for new articles, prepend relevant past thoughts to the input.
- This keeps the agency intact while ensuring continuity.
Instead of a long, instruction-heavy prompt, show a few short, well-crafted examples of how a tree journalist has written in the past. The LLM will generalize from those.
If the article is too general or meandering, you can set contextual constraints dynamically:
- “Write this article as if you were a tree living in a rainforest.” (vs. a desert, city park, etc.)
- “How would a 500-year-old oak tree journalist write this differently from a young sapling?”
- This keeps it flexible but gives subtle guidance.
The key is progressive prompting—not overwhelming the model all at once but letting it develop ideas iteratively. By having the tree “think” in stages and engage in self-reflection, you’ll get much more coherent and compelling articles without stripping away its agency.
Would love to hear how this approach fits with your workflow!