RAG reranking is a post-retrieval step that reorders candidate documents by true relevance before passing them to an LLM. Initial retrieval (vector, keyword, or hybrid) returns a broad candidate set, then a reranker—typically a cross-encoder—scores each query-document pair and promotes the most relevant results. The post covers types of rerankers (cross-encoder, bi-encoder, LLM-based, hybrid), the problems reranking solves (hallucinations, context dilution, poor top-k ordering), common challenges (latency, cost, scaling), evaluation metrics (Precision@k, Recall@k, MRR), when reranking is unnecessary, and best practices for building a reranking pipeline. Tools covered include Meilisearch, Cohere, Hugging Face, Pinecone, and Weaviate.
Table of contents
What is RAG reranking?Why does RAG reranking matter?How does RAG reranking work?What types of rerankers exist?What problems does RAG reranking solve?What are common RAG reranking challenges?How do you evaluate the quality of RAG reranking?How does RAG reranking compare to hybrid search?How do you choose a RAG reranker?What tools support RAG reranking?How does Meilisearch support RAG reranking?When is reranking unnecessary in RAG?What are the best practices for RAG reranking?What RAG reranking means for building better RAG systemsSort: