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

Short summary

A detailed technical guide on rebuilding a RAG retrieval layer from first principles, covering chunking strategies (fixed, recursive, semantic, agentic), hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. The approach achieved 95% recall@10 and cut latency by 40% through parallel retrieval and query transformation. Includes production configs by document type with measured recall metrics.

• •Four chunking strategies benchmarked by document type with recall@10 metrics
• •Hybrid retrieval (vector + BM25 + cross-encoder rerank) beats either approach alone
• •Query transformation and parallel retrieval cut latency 40% while reaching 95% recall@10

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