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 away from basic semantic search, a new measured, tunable retrieval pipeline was developed from first principles. This approach addresses issues in legal contracts, API docs, and customer tickets where fixed windows fail. The implementation of Bayesian Search successfully reduced latency by 40% and achieved a 95% recall@10, overcoming the performance bottlenecks of traditional embedding and vector search processes.