Your RAG Finds the Documents. But Which Ones Should Reach the LLM?

In Retrieval-Augmented Generation (RAG) systems, the initial retrieval process often gathers a large volume of document chunks, not all of which are equally relevant to a user's query. This article explains the critical role of reranking in optimizing RAG pipelines. While the retriever efficiently narrows down a vast database to a set of candidate chunks using vector embeddings, the reranker—typically a cross-encoder model—performs a more precise evaluation. By pairing the query with each individual chunk, the reranker assigns a relevance score, allowing developers to sort and prioritize the most pertinent information before sending it to the Large Language Model (LLM). This two-step approach of fetching followed by ranking ensures that the LLM receives the highest quality context, ultimately improving the accuracy and effectiveness of AI-generated responses in complex systems.