How to build RAG at scale

"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."

"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."

Retrieval-augmented generation (RAG) grounds generative AI in internal knowledge to reduce hallucinations and unlock value from documents, policies, tickets, and institutional memory. Proofs of concept are common, but reliable production RAG fails due to systems architecture issues rather than model quality. Major challenges include ingestion, retrieval optimization, metadata management, versioning, indexing, evaluation, and long-term governance. Knowledge is messy, evolving, contradictory, and distributed across wikis, PDFs, spreadsheets, APIs, ticketing systems, and chat. Scalable RAG requires robust ingestion pipelines with normalization, cleaning, chunking, version control, authoritative metadata, and consistent heuristics to prevent stale or conflicting information from causing hallucinations.

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