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

Standard RAG implementations often fail in production due to fixed chunking and high latency. By moving beyond basic semantic search and text-embedding-3-small, the team rebuilt their retrieval layer from first principles. This transition to a measured, tunable pipeline addressed issues with legal contracts and API docs. The implementation of Bayesian Search successfully cut latency by 40% and achieved a 95% recall@10, solving the performance bottlenecks associated with embedding and vector search processes.