The RAG Chunking Dilemma: From Document Fragmentation to Intelligent Retrieval Architecture

As LLM applications scale, most RAG failures stem not from weak models but from poor chunking strategy—the overlooked decision that directly impacts retrieval accuracy and hallucination rates. This guide reveals why chunking is less about splitting documents and more about preserving semantic meaning while optimizing context windows and retrieval precision. Understanding the progression from basic chunking through Agentic RAG and GraphRAG equips builders to match architecture complexity to query demands and operational constraints.