Chunking strategies
Skill Amey-Thakur/AI-SKILLS/skills/ai-memory-rag/chunking-strategies
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.From its SKILL.md
npx -y skills add Amey-Thakur/AI-SKILLS --skill chunking-strategiesAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
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SKILL.md
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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
- Split on structure before size. Headings, sections, and paragraphs are meaning boundaries the author already provided, and respecting them beats any fixed character count.
- 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.
- 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.
- 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.
- 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).
- 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).
- 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.