How do you choose the optimal chunking strategy for RAG?

Chunking strategy significantly impacts retrieval quality. Methods: fixed-size chunking (split at character/token count, simplest but may break mid-sentence), recursive character splitting (split at paragraph, then sentence, then word boundaries, respecting natural text structure), semantic chunking (split when embedding similarity between consecutive sentences drops below threshold, creating semantically coherent units), and document-structure-aware chunking (use headings, sections, and document hierarchy to define chunk boundaries). Size considerations: smaller chunks (256-512 tokens) improve retrieval precision but may lack context, larger chunks (1024-2048 tokens) provide more context but reduce precision. The sweet spot depends on content type: technical documentation benefits from smaller, focused chunks; narrative content benefits from larger chunks that preserve flow. Overlap: 10-20% overlap between consecutive chunks prevents information loss at boundaries. Metadata enrichment: attach parent document context to each chunk (document title, section hierarchy, page number), enabling the retrieval system to provide broader context when needed. Advanced patterns: parent-child chunking (store small chunks for retrieval but expand to parent section for generation), sliding window with stride (overlapping windows capture cross-boundary information), and multi-granularity indexing (index at both paragraph and section level, retrieve at the level most relevant to the query). Evaluation: test chunking strategies on a representative query set, measure retrieval precision@k and answer quality, and iterate. There is no universally optimal chunking strategy; always test with your specific data and queries.