13 RAG chunking strategies for better retrieval

This post introduces and details 13 distinct strategies for chunking documents in RAG systems to improve retrieval accuracy. It explains the importance of chunking for performance, accuracy, and cost reduction, and outlines evaluation metrics like precision, recall, and context preservation. Specific strategies discussed include fixed-length, sentence-based, semantic-based, paragraph-based, sliding-window, recursive, hierarchical, context-enriched, topic-based, modality-specific, agentic, subdocument, and hybrid chunking, with pros, cons, and best use cases for each.