RAG Explained: Reranking for Better Answers

Standard retrieval-augmented generation (RAG) pipelines can retrieve documents with high similarity scores that are not actually relevant for answering a user's query. Reranking is introduced as a second stage to refine these initial results, ensuring only the most useful information is passed to the large language model. The primary method for reranking involves using cross-encoders, which jointly embed the query and each document to produce a more accurate relevance score, though this process is computationally expensive. This leads to a two-stage retrieval process where a fast initial retrieval narrows down candidates, and a slower, more accurate cross-encoder then reranks this smaller set for optimal relevance.