Developers building RAG AI applications use various document chunking strategies to break large documents into smaller pieces before embedding and storing in vector databases. Effective chunking improves retrieval precision, reduces hallucinations, and lowers inference costs. Common strategies include fixed-size chunking for prototypes, recursive chunking for general RAG systems, overlapping chunks for production systems, semantic chunking for enterprise search, and structure-aware chunking for documentation and code. Hybrid approaches combining structure-aware splitting, recursive chunking, and overlap are used in production to balance relevance, cost, and simplicity.

May 24, 2026, 3:00 PM