Retrieval-augmented technology (RAG) has shortly turn out to be the enterprise default for grounding generative AI in inner data. It guarantees much less hallucination, extra accuracy, and a technique to unlock worth from many years of paperwork, insurance policies, tickets, and institutional reminiscence. But whereas practically each enterprise can construct a proof of idea, only a few can run RAG reliably in manufacturing.
This hole has nothing to do with mannequin high quality. It’s a programs structure downside. RAG breaks at scale as a result of organizations deal with it like a characteristic of massive language fashions (LLMs) reasonably than a platform self-discipline. The actual challenges emerge not in prompting or mannequin choice, however in ingestion, retrieval optimization, metadata administration, versioning, indexing, analysis, and long-term governance. Data is messy, consistently altering, and sometimes contradictory. With out architectural rigor, RAG turns into brittle, inconsistent, and costly.
RAG at scale calls for treating data as a dwelling system
Prototype RAG pipelines are deceptively easy: embed paperwork, retailer them in a vector database, retrieve top-k outcomes, and move them to an LLM. This works till the primary second the system encounters actual enterprise habits: new variations of insurance policies, stale paperwork that stay listed for months, conflicting information in a number of repositories, and data scattered throughout wikis, PDFs, spreadsheets, APIs, ticketing programs, and Slack threads.
