The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%"

Short summary

A detailed engineering walkthrough of optimizing a production RAG pipeline across chunking, retrieval, and hyperparameter tuning. Key techniques include document-type-aware chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search with BM25 via reciprocal rank fusion and cross-encoder reranking, LLM-based query expansion, and Bayesian optimization with Optuna to auto-tune parameters. The result: 95% recall@10 and 40% latency reduction versus default RAG configurations.

• •Document-type-aware chunking (recursive, semantic, agentic) beats fixed 512-token splits across legal, API docs, tickets, and wikis
• •Hybrid retrieval with RRF + cross-encoder reranking lifts recall 15% over pure vector search
• •Bayesian optimization via Optuna replaces manual parameter guessing, achieving 95% recall@10 and 40% latency cut

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