RAG reranking explained: better context, better answers

What this post added

This post introduces RAG reranking as a post-retrieval step to reorder search results by true relevance, improving the quality of context provided to LLMs and reducing hallucinations. It details how reranking works, the different types of rerankers (cross-encoder, lightweight, LLM-based, hybrid) and their trade-offs in terms of accuracy, latency, and cost. It also discusses common challenges like latency, cost, scaling, and evaluation.