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Retrieval-augmented generation (RAG) has rapidly change into the enterprise default for grounding generative AI in inner information. It guarantees much less hallucination, extra accuracy, and a approach to unlock worth from a long time of paperwork, insurance policies, tickets, and institutional reminiscence. But whereas almost 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 techniques structure drawback. RAG breaks at scale as a result of organizations deal with it like a function of large language models (LLMs) somewhat 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, continuously altering, and infrequently contradictory. With out architectural rigor, RAG turns into brittle, inconsistent, and costly.
RAG at scale calls for treating information as a dwelling system
Prototype RAG pipelines are deceptively easy: embed paperwork, retailer them in a vector database, retrieve top-k outcomes, and cross 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 information scattered throughout wikis, PDFs, spreadsheets, APIs, ticketing techniques, and Slack threads.
