Retrieval-augmented generation (RAG) has quickly become the enterprise default for grounding generative AI in internal knowledge. It promises less hallucination, more accuracy, and a way to unlock value from decades of documents, policies, tickets, and institutional memory. Yet while nearly every enterprise can build a proof of concept, very few can run RAG reliably in production.
This gap has nothing to do with model quality. It is a systems architecture problem. RAG breaks at scale because organizations treat it like a feature of large language models (LLMs) rather than a platform discipline. The real challenges emerge not in prompting or model selection, but in ingestion, retrieval optimization, metadata management, versioning, indexing, evaluation, and long-term governance. Knowledge is messy, constantly changing, and often contradictory. Without architectural rigor, RAG becomes brittle, inconsistent, and expensive.
RAG at scale demands treating knowledge as a living system
Prototype RAG pipelines are deceptively simple: embed documents, store them in a vector database, retrieve top-k results, and pass them to an LLM. This works until the first moment the system encounters real enterprise behavior: new versions of policies, stale documents that remain indexed for months, conflicting data in multiple repositories, and knowledge scattered across wikis, PDFs, spreadsheets, APIs, ticketing systems, and Slack threads.
