The original title is: "Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%"

Short summary

A detailed engineering walkthrough of rebuilding a RAG retrieval layer for production, covering chunking strategies by document type, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. The author reports achieving 95% recall@10 and 40% latency reduction by moving beyond naive semantic search. Includes production config tables and Python code for each pipeline stage.

• •Chunking strategy should vary by document type: recursive for legal/API docs, semantic for conversations, agentic for complex wikis
• •Hybrid retrieval (vector + BM25 + cross-encoder rerank) significantly outperforms pure vector or keyword search
• •Query transformation before retrieval improves results because users ask badly; cross-encoder rerank adds 15% recall for 50ms cost

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