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

Short summary

A detailed engineering breakdown of rebuilding a RAG retrieval layer for production, covering four chunking strategies (fixed, recursive, semantic, agentic) tuned per document type with measured recall@10 metrics. Hybrid retrieval combines vector search and BM25 via Reciprocal Rank Fusion, then cross-encoder reranking funnels 50 candidates to 5 with 15% recall gain. Query transformation and Bayesian optimization reportedly cut latency by 40% while achieving 95% recall@10.

• •Four chunking strategies mapped to document types with recall@10 metrics (91-97%)
• •Hybrid retrieval via vector + BM25 + reciprocal rank fusion + cross-encoder rerank
• •Query transformation and Bayesian search optimization cut latency 40% at 95% recall@10

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