Retrieval

Improve RAG Retrieval with Reranking

Retrieval-augmented generation (RAG) depends on finding useful evidence before asking a language model to answer. A vector search can retrieve candidates quickly, but the nearest vectors are not always the passages that best answer the user’s question. Reranking adds a second relevance step. The system first retrieves a reasonably broad candidate set with a fast method, then applies a more precise model to reorder those candidates before selecting context for the LLM.