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

Short summary

A detailed engineering breakdown of rebuilding a RAG retrieval layer from first principles, covering document-type-specific chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking that improves recall from ~75% to 92% correlation. The pipeline achieves 95% recall@10 with a 40% latency reduction through parallel retrieval and query transformation.

• •Document-type-aware chunking: recursive for legal, semantic for tickets, agentic for wikis
• •Hybrid retrieval (vector + BM25 + cross-encoder rerank) beats either method alone
• •Query transformation and parallel retrieval cut latency 40% while reaching 95% recall@10

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