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

Short summary

A practical deep dive into rebuilding a RAG retrieval layer for production, covering four chunking strategies (fixed, recursive, semantic, agentic) with per-document-type configs achieving 91-97% recall@10. The pipeline combines parallel vector and BM25 search with reciprocal rank fusion, then cross-encoder reranking (50→5 funnel) for a 15% recall gain at 50ms cost. Query transformation expands user questions into multiple search queries to handle poorly-phrased inputs, with the overall system cutting latency by 40%.

• •Four chunking strategies tuned per document type yield 91-97% recall@10 in production
• •Hybrid retrieval (vector + BM25 + RRF + cross-encoder rerank) beats either approach alone
• •Query transformation and a 50→5 rerank funnel add 15% recall while cutting latency 40%

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