Split documents into retrieval units that preserve meaning, so a retrieved chunk answers the question rather than trailing off mid-thought. Use when building or fixing a retrieval pipeline whose results are technically relevant but useless.
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---
name: chunking-strategies
description: Split documents into retrieval units that preserve meaning, so a retrieved chunk answers the question rather than trailing off mid-thought. Use when building or fixing a retrieval pipeline whose results are technically relevant but useless.
---
# Chunking strategies
Chunking decides what retrieval can ever return. A chunk that splits a
definition from its example, or a table from its header, cannot be
rescued by a better embedding model or a smarter reranker. Most RAG
quality problems are chunking problems.
## Method
1. **Split on structure before size.** Headings, sections, and
paragraphs are meaning boundaries the author already provided, and
respecting them beats any fixed character count.
2. **Size to the question, not to the model's limit.** Chunks large
enough to contain a complete answer and small enough that most of the
chunk is relevant, which is usually far below the context window.
3. **Overlap modestly at boundaries.** A small overlap prevents an
answer that straddles a split from being lost, at the cost of some
duplication in results.
4. **Carry context into the chunk.** Document title, section heading,
and date prepended to the text, since a retrieved chunk arrives
without its surroundings and must stand alone.
5. **Treat tables, code, and lists as units.** Splitting them produces
fragments that are worse than useless because they look authoritative
and are incomplete (see document-parsing).
6. **Keep a link back to the source location.** Every chunk needs enough
provenance to cite and to let a user open the original (see
citation-grounding).
7. **Evaluate chunking as a variable.** Re-index with different
strategies and measure retrieval quality, because intuition about
chunk size is usually wrong (see rag-evaluation).
## Boundaries
Chunking shapes retrieval; it cannot compensate for content that does
not contain the answer. Re-chunking requires full reindexing, so the
strategy is expensive to change late (see rag-freshness). Different
document types in one corpus may need different strategies rather than
one compromise.
