Why RAG Chunks Are Made to Overlap With Each Other

Why documents get chunked at all

Retrieval-augmented generation (RAG) systems split long documents into smaller pieces before turning each one into a searchable embedding vector, since a whole long document embedded as a single vector tends to blur together too many different topics to be useful for precise retrieval.

The problem with a clean, non-overlapping split

If chunks are cut with no overlap at all, an important sentence or idea that happens to fall exactly on a chunk boundary gets split in half — the end of one chunk might contain a broken, out-of-context sentence fragment, and the next chunk starts with the other broken half. Whichever chunk actually gets retrieved for a query may be missing exactly the piece of context that made it relevant.

How overlap fixes this

Making each chunk start partway into the end of the previous one (a deliberate overlap) means content near a boundary appears complete in at least one of the two neighboring chunks, even if it's split awkwardly in the other. A modest overlap — commonly somewhere around 10-20% of the chunk size — is usually enough to substantially reduce this problem without creating excessive duplicate content across the index.

Try it yourself

Our RAG Chunk Calculator lets you preview exactly how a document would be split for a given chunk size and overlap, before you build it into your actual pipeline.