13 RAG chunking strategies for better retrieval
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A comprehensive breakdown of 13 RAG chunking strategies, covering fixed-length, sentence-based, semantic, paragraph, sliding window, recursive, hierarchical, context-enriched, topic-based, modality-specific, agentic, subdocument, and hybrid approaches. Each strategy includes pros, cons, best-fit use cases, and Python implementation examples. Also covers how to choose chunk size, overlap guidelines (10–20%), evaluation methods using precision and recall, common mistakes like boundary problems and context loss, and best practices for balancing cost, accuracy, and complexity.
Table of contents
What is RAG chunking?Why is chunking important in RAG?What is the best chunking strategy for RAG?What are the different chunking strategies for RAG?How do you choose chunk size?How much overlap should chunks have?What are RAG chunking best practices?How do document types affect chunking?How do you evaluate chunking performance?What are common chunking mistakes in RAG?How to choose the right RAG chunking strategy for your use case89 Impressions