IDInternals Decodedininternals-decoded.hashnode.dev·11h ago · 10 min readEvaluating RAG: Retrieval Metrics That Predict Answer QualityA RAG (retrieval-augmented generation) pipeline lives or dies on whether the retriever finds the right evidence. The metrics that actually predict answer quality are recall under your token budget, co00
IDInternals Decodedininternals-decoded.hashnode.dev·2d ago · 11 min readGrounding and Citations: Answers You Can CheckIn Part 5 we saw how hybrid search and re-ranking can dramatically improve retrieval quality. Now we tackle the next challenge: making sure the answers built from those retrieved chunks are actually t00
IDInternals Decodedininternals-decoded.hashnode.dev·4d ago · 13 min readHybrid Search and Re-Ranking: The Cheapest Quality WinHybrid search runs two retrieval methods in parallel, BM25 for exact keyword matching and vector search for semantic similarity, then fuses their results using reciprocal rank fusion. Adding a cross-e00
IDInternals Decodedininternals-decoded.hashnode.dev·6d ago · 12 min readVector Databases: What Actually Matters When ChoosingVector databases look remarkably similar on the outside. Most wrap the same open-source ANN libraries and offer search, insert, delete over HTTP. The parts that actually change your RAG (retrieval-aug00
IDInternals Decodedininternals-decoded.hashnode.dev·Sep 24 · 13 min readEmbeddings and Vector Search, DemystifiedEmbedding models convert your company handbook’s text chunks into high-dimensional vectors, and approximate nearest neighbor (ANN) indexes like HNSW or IVF-PQ retrieve the most semantically similar ch00
