Writing Docs That Retrieve Well
Retrieval quality starts with document quality.
The prompt
Restructure this document for AI retrieval: break it into sections with descriptive question-style headings, one topic per section, key facts stated explicitly (no "as mentioned above" references). Keep all information, change only structure. [paste document]
What to replace
Swap these placeholders for your own details before running the prompt:
[paste document]your own value
Pro tip: Every section should make sense read in isolation — retrieval pulls sections out of context, so "see above" references break answers.
How to use this prompt
- Copy the prompt using the button above.
- Replace [paste document] with your own details — the more specific you are, the better the output.
- Paste it into Claude and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Learn the technique
Writing Docs That Retrieve Well
Module 2 — No-Code Custom Knowledge · RAG & Custom AI Knowledge
Related prompts
What RAG Actually Does
RAG (Retrieval-Augmented Generation) retrieves the most relevant passages from your documents and injects them into the prompt before the model answers.
ChatGPTChunking: Why Split Size Matters
Documents are split into chunks before embedding.
ChatGPT / ClaudeCustom GPTs & Claude Projects
Both ChatGPT (Custom GPTs) and Claude (Projects) let you upload documents and set standing instructions — a no-code RAG setup.
ChatGPTSQL Query Explainer
Understanding or debugging unfamiliar queries. Ideal when you inherit a legacy codebase, review a teammate's pull request, or hit a slow query you did not write. Asking for a plain-English walkthrough before the optimisation notes means you understand the intent first, so you can tell a genuine bug from a deliberate design decision.
ClaudeCode Review Pass
Pre-review pass before submitting a pull request
ClaudeTone Rewriter
Adjusting tone without rewriting from scratch