13 RAG chunking strategies for better retrieval

What this post added

This post introduces and details 13 distinct strategies for chunking data in Retrieval-Augmented Generation (RAG) systems. It explains the importance of chunking for RAG performance and accuracy, categorizes chunking into umbrella strategies (fixed size, sentences, overlapping, semantic), and provides detailed explanations, pros, cons, best use cases, and implementation examples for each of the 13 specific strategies: fixed-length, sentence-based, semantic-based, paragraph-based, sliding-window, recursive, hierarchical, context-enriched, topic-based, modality-specific, agentic, subdocument, and hybrid chunking. It also discusses metrics for evaluating chunking performance (precision, recall, context preservation) and common pitfalls.