Optimizing RAG at Scale: Chunking Strategies, Hybrid Retrieval, and Latency Reduction

Short summary

A detailed engineering walkthrough of rebuilding a production RAG retrieval layer from first principles, achieving 95% recall@10 and 40% latency reduction. Covers four chunking strategies (fixed, recursive, semantic, agentic) mapped to document types, hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. Includes production config tables with measured recall metrics per document type.

• •Four chunking strategies (fixed, recursive, semantic, agentic) mapped to document types with measured recall@10 metrics
• •Hybrid retrieval combines vector search and BM25 via reciprocal rank fusion, then cross-encoder reranks top 50 to 5
• •Query transformation layer handles poorly-formed user questions before retrieval

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