RAG at Enterprise Scale

Every RAG tutorial covers the same ground: chunk your documents, embed them, store in a vector database, retrieve top-k results, pass to the model. This is sufficient for a demo. It is not sufficient for production.

The production RAG decisions that determine whether a system is useful; chunking strategy for heterogeneous document types, hybrid retrieval that combines dense and sparse signals, re-ranking to surface the most relevant chunks after initial retrieval, query decomposition for complex multi-part questions, citation integrity, latency at scale; none of these appear in the tutorials.

This whitepaper covers the production decisions Tenten AI has made across 20+ enterprise RAG deployments in financial services, healthcare, legal, and manufacturing. It offers an opinionated guide to the decisions that matter most and the reasoning behind them, rather than a comprehensive survey of the field.