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Pratik Dhanave · ·17 min read

Making RAG Good

Why the naive RAG pipeline from post 9 underperforms in production, and the concrete, evaluation-driven fixes — structure-aware chunking, hybrid search, reranking, query transformation, and deliberate context construction — each explained with the reasoning and a real Go sketch.

Why naive RAG underperforms and the techniques that fix it: measure recall@k first, then structure-aware chunking, hybrid dense+BM25 search fused with RRF, over-retrieve-then-rerank, query transformation (HyDE, multi-query), and deliberate context construction against lost-in-the-middle.

#AI Engineering#RAG#Go#Search#Backend Engineering

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.