Chunking: Why Split Size Matters
Documents are split into chunks before embedding.
The prompt
Split this document into retrieval-ready chunks. Rules: each chunk 200-400 words, never split mid-paragraph, prepend each chunk with a 1-line context header stating the document title and section it came from. Return as a numbered list. [paste document]
What to replace
Swap these placeholders for your own details before running the prompt:
[paste document]your own value
Pro tip: Prepending a short context header to every chunk ("From: 2025 Pricing Policy > Refunds") dramatically improves retrieval accuracy for ambiguous queries.
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 ChatGPT 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
Chunking: Why Split Size Matters
Module 1 — RAG Fundamentals · RAG & Custom AI Knowledge
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Chunking
Chunking splits documents into smaller passages before embedding them for retrieval. Chunk size is a key quality lever: too large dilutes relevance, too small loses the context needed to make sense.
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