RAG Explained: Reranking for Better Answers

Reranking improves retrieval-augmented generation by addressing limitations of similarity-based retrieval. While vector search quickly narrows down candidate chunks, it may retrieve semantically similar but irrelevant content. Cross-encoders provide more accurate relevance assessment by jointly embedding queries and documents, though they're computationally expensive. A two-stage approach combines fast vector search with precise cross-encoder reranking, creating an efficient pipeline that delivers higher quality responses by filtering the most relevant context for language models.