What Is Chunking in RAG? Splitting Documents for Retrieval
A whole document is usually too large and unfocused to retrieve as one unit — chunking splits it into smaller pieces that can be searched and retrieved individually.
Breaking documents into retrievable pieces
Chunking splits a longer document into smaller, more focused pieces before it's stored for retrieval in a RAG system — each chunk gets its own embedding and can be retrieved independently, rather than treating an entire document as one indivisible unit.
Why chunk size is a real tuning problem
Chunks that are too large dilute a single embedding with too many different ideas at once, hurting retrieval precision. Chunks that are too small lose surrounding context that might be needed to make sense of the piece on its own. Getting this balance right is one of the most consequential tuning decisions in a RAG pipeline.
Smarter-than-naive chunking
Naive chunking just splits every N characters, which can cut a sentence or idea awkwardly in half. More careful approaches split along natural boundaries — paragraphs, sections, sentences — and sometimes overlap chunks slightly so context isn't lost right at a cut boundary.
Frequently Asked Questions
What is chunking in RAG?
The process of splitting a document into smaller pieces before storing it for retrieval, so each chunk can be searched and retrieved individually instead of treating the entire document as one indivisible unit.
Why does chunk size matter?
Chunks too large dilute retrieval precision with too many mixed ideas in one embedding; chunks too small lose context needed to understand the piece on its own. Tuning this balance significantly affects retrieval quality.