Retrieval-Augmented Generation (RAG) is often described as a simple pipeline: Query → Retrieve documents → Send context to an LLM → Generate answer In production, however, retrieval is rarely that simple. The retriever can return irrelevant documents. Important information may be buried in the middle of a document. A query may be too vague for semantic search. Retrieved chunks may lose their surrounding context. And sometimes the model does not need retrieval at all. The quality ...