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

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To optimize RAG at scale, a retrieval layer was rebuilt from first principles, moving beyond basic semantic search. Standard methods using 512-token chunks and text-embedding-3-small often fail in production with legal contracts or API docs. By implementing a measured, tunable pipeline and Bayesian Search, the system achieved a 95% recall@10 and reduced latency by 40%. This approach addresses critical issues where fixed windows previously split clauses or drowned signals in noise during processing.
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