5 Chunking Strategies For RAG

Five chunking strategies for Retrieval Augmented Generation (RAG) are explained: fixed-size chunking splits text into uniform segments with overlap, semantic chunking groups segments based on embedding similarity, recursive chunking uses natural separators then splits oversized chunks, document structure-based chunking follows inherent document organization, and LLM-based chunking uses language models for semantic accuracy. Each approach has trade-offs between simplicity, semantic preservation, and computational cost, with semantic chunking often performing well in practice.