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 and text-embedding-3-small, a new retrieval pipeline was developed from first principles. This measured, tunable approach addresses issues with legal contracts, API docs, and customer tickets. The implementation of Bayesian Search successfully cut latency by 40% while achieving a 95% recall@10, overcoming the limitations of traditional 512-token chunking and slow vector search processes.