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

The team optimized RAG at scale by replacing basic semantic search with a tunable retrieval pipeline, achieving 95% recall@10 and cutting latency by 40%. They addressed production failures where fixed 512-token chunks disrupted legal contracts and API docs. By rebuilding the retrieval layer from first principles, they overcame inefficiencies in text-embedding-3-small and vector search. This transition moved the system from a demo-level setup to a measured architecture capable of handling complex conversational contexts.