#RAG

How reranking turns high-recall retrieval into high-precision context: cross-encoders vs bi-encoders, where rerankers fit in a RAG pipeline, and the cost.

How vector search actually retrieves text: dense embeddings vs sparse keyword search, why hybrid wins, and how to fuse the two with reciprocal rank fusion.

What RAG is, when to use it, and how the retrieval pipeline actually works — chunking, embeddings, hybrid search, reranking, and evaluation, end to end.

Most failing RAG systems don't have a model problem, they have a retrieval problem. Here's how chunking, embeddings, and reranking actually decide whether your answers are any good.

Your RAG retrieval quality decays silently as data, models, and queries shift. A practical guide to detecting embedding drift and re-indexing safely.