What chunking strategies exist and how to choose one?

Choosing the right chunking strategy for document processing in retrieval-augmented generation (RAG) pipelines or semantic search systems is crucial, as the wrong choice can degrade retrieval quality. Various strategies, such as fixed-size, recursive/sentence-aware, semantic, document-structure, and sliding window chunking, each offer distinct tradeoffs and are suited to different types of documents and retrieval precision needs. Fixed-size chunking is simple and fast but may split sentences awkwardly, making it suitable for prototyping. Recursive chunking preserves sentence integrity and is good for general prose, while semantic chunking aligns with topic shifts, offering high precision for complex documents but at a higher computational cost. Document-structure chunking uses inherent document markers like headers, making it ideal for structured documents, while sliding window chunking ensures continuity for conversational text but increases redundancy. The decision framework for selecting a strategy considers document type, retrieval precision requirements, and infrastructure constraints, with no single strategy universally best, emphasizing empirical evaluation with actual queries and documents.