Reranking in RAG: How to Fix Bad Retrieval with a Reranker
When a RAG (Retrieval-Augmented Generation) app gives wrong answers even though the right information is in your documents, retrieval is often the cause. Vector search is great at finding chunks on the right topic, but it often falls for near misses, like pulling the bucket limit for the Enterprise plan when the user asked about the Growth plan. Reranking in RAG fixes this by taking your vector search’s top 30 candidates and passing them through a dedicated cross-encoder model that re-scores each chunk directly against the question.
