You're doing RAG wrong

There's a new approach that: cuts corpus size by 40x. reduces tokens per query by 3x. improves vector search relevance by 2.3x And it doesn't touch your retrieval algorithm, your reranker, or your embedding model. It fixes something upstream that almost no one examines Every RAG pipeline starts with the same assumption: a chunk of text is the right unit of knowledge to embed That assumption is almost never examined And it's the source of most of the retrieval failures people try to fix downstrea