Why Chunking Is the Most Critical Yet Overlooked Step in RAG Systems

In Retrieval-Augmented Generation (RAG) systems, chunking — the process of splitting large documents into smaller segments before embedding — is often rushed but has an outsized impact on overall performance. Embedding models cannot effectively capture a full document in a single vector, making smaller, focused chunks essential for accurate retrieval. Three primary chunking strategies exist: fixed-size, which cuts at set character limits regardless of context; recursive, which respects natural text boundaries like paragraphs and sentences; and semantic, which uses a model to detect topic shifts and cut accordingly. Each approach involves trade-offs between speed, simplicity, and contextual coherence. Errors introduced at the chunking stage propagate through the entire pipeline, making it a foundational decision rather than a trivial preprocessing step.