Why your RAG pipeline's chunking strategy matters more than you think

In retrieval-augmented generation (RAG) pipelines, document chunking — how text is split before embedding — is often left at default settings, yet it fundamentally determines answer quality. Fixed-size chunkers that split text by character or token count can sever critical context, such as cutting off a policy exception mid-sentence, leading to factually incomplete responses even when all other components work correctly. Embedding models compress each chunk into a single vector point representing its average meaning, meaning small chunks offer precise retrieval but lack context, while large chunks preserve context but dilute similarity scores. Developers are advised to treat chunk size as a deliberate design decision rather than a default, with 200–500 tokens suggested as a reasonable starting range for prose documentation. Structural chunking strategies — such as keeping entire document sections together — can prevent context loss without requiring changes to the underlying models or infrastructure.