How would you choose a chunking strategy for a RAG system?

Balances retrieval precision, answer completeness, document structure, overlap, metadata, and model input limits.

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TL;DR

I start from the document's natural structure, such as headings, paragraphs, procedures, or table rows, and keep each chunk within the embedding model's input limit. Chunks need enough surrounding context to stand on their own but should not mix unrelated topics. I choose size and overlap using representative retrieval queries rather than copying a universal token number.

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Practice focus

Whether chunking is tuned to document semantics and measured retrieval behavior.

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Answer path

Core answer first, then checkpoints toward a stronger answer.

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Core answer
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Overview
Chunk size is a retrieval decision: small units improve precision, while larger units preserve context.
I start from the document's natural structure, such as headings, paragraphs, procedures, or table rows, and keep each chunk within the embedding model's input limit. Chunks need enough surrounding context to stand on their own but should not mix unrelated topics. I choose size and overlap using representative retrieval queries rather than copying a universal token number.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
For a product manual, I might keep a short procedure together even if it spans several paragraphs, then carry the title and section path as metadata. Overlap can preserve meaning across a boundary, but excessive overlap duplicates results, increases index size, and crowds the context with near-identical text. I compare retrieval recall and answer support across chunking variants, including questions whose evidence crosses a boundary.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Using one fixed size for every document type.
Adding large overlap without measuring duplicate retrieval.
Splitting away headings and source context.

How would you choose a chunking strategy for a RAG system?

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