Research

RAG: Coming together at the Seams

Retrieval-augmented generation (RAG) answers questions by searching a collection of documents and passing the best-matching passages to a language model. Before a document can be searched, it is cut into pieces called chunks, and every cut is a place where an idea can be split in two. This paper describes that boundary problem, reviews how current chunking strategies try to manage it, and identifies the gap they leave: a fact that is split across two large chunks may not be found at all, because neither half resembles the question closely enough. It then introduces Seams, a chunking strategy that indexes a small, separate search vector over every chunk boundary and links it to both chunks it straddles.