chunking-strategies

Document chunking divides large texts into optimal-sized pieces for retrieval-augmented generation. Chunk size, overlap, and method (fixed-size, semantic, or recursive) depend on document type and retrieval requirements to preserve semantic boundaries and improve LLM response quality.

Technical documentation benefits from recursive chunking that respects code blocks and section hierarchies. This approach maintains semantic boundaries, enables hierarchical reconstruction, and improves retrieval precision for structured content.

Chunk enrichment involves adding metadata fields, LLM-generated summaries, keywords, and parent IDs to each chunk. This context improves retrieval quality and helps the LLM understand relationships between chunks and their source documents.

Fixed-size chunking with overlap can work for legal documents, but semantic or recursive methods better preserve legal language boundaries and clause structures. Document-type-specific strategies yield higher retrieval quality than uniform approaches.

Yes. Chunking strategies vary by document type—code and technical docs use recursive methods with smaller chunks, conversations use semantic boundaries by turn, and legal documents require larger overlaps. Tailor size and method to content structure and retrieval patterns.

Evaluate chunking by measuring retrieval precision and relevance of LLM responses against your document corpus. Adjust chunk size, overlap, and method based on retrieval performance; best practices include semantic boundary preservation and metadata inclusion.