RAG reranking explained: better context, better answers

This post introduces RAG reranking as a post-retrieval step to reorder search results by true relevance before they reach the language model. It explains how reranking improves the quality of context provided to LLMs, reduces hallucinations, and addresses context dilution and poor top-k retrieval. The post details the two stages of reranking (initial retrieval and reranker evaluation), the role of cross-encoder models in scoring and reordering documents, and the different types of rerankers (cross-encoder, lightweight, LLM-based, hybrid) with their respective advantages and disadvantages. It also discusses common challenges such as latency, cost, scaling, and infrastructure complexity associated with implementing RAG reranking.