RAG and the Art of Not Dropping the Answer
RAG and the Art of Not Dropping the Answer A RAG team usually starts with a familiar ambition: make the retrieved context smarter. The raw document feels too long. The search snippet feels too primitive. The page structure looks messy. A query-focused summary sounds more elegant. A proposition list sounds more machine-readable. A paraphrase from a strong LLM sounds, at least cosmetically, like an upgrade. So the team builds another representation layer between retrieval and generation, hoping the model will reward the extra sophistication. ...