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.
#RAG
5 articles
· 8 min read
AI EngineeringRead article· 8 min read
· 5 min read
AI EngineeringHow vector search actually retrieves text: dense embeddings vs sparse keyword search, why hybrid wins, and how to fuse the two with reciprocal rank fusion.
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· 7 min read
AI EngineeringWhat RAG is, when to use it, and how the retrieval pipeline actually works — chunking, embeddings, hybrid search, reranking, and evaluation, end to end.
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· 5 min read
AI EngineeringMost 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.
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· 5 min read
AI EngineeringYour RAG retrieval quality decays silently as data, models, and queries shift. A practical guide to detecting embedding drift and re-indexing safely.
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