Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%

Short summary

A detailed walkthrough of rebuilding a production RAG retrieval pipeline from first principles, covering chunking strategies by document type, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. The approach achieved 95% recall@10 and cut latency 40% by using parallel retrieval stages and a 50-to-5 reranking funnel. Includes production config tables and Python code for each component.

• •Document-type-specific chunking beats fixed 512-token splits — recursive, semantic, and agentic strategies each have optimal use cases
• •Hybrid retrieval (vector + BM25 + cross-encoder rerank) via reciprocal rank fusion achieves 95% recall@10
• •Cross-encoder reranking costs 50ms but gains 15% recall over bi-encoder-only approaches

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