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Learn how to scale Retrieval Augmented Generation (RAG) from prototype to production with trusted retrieval, governance, security, observability, and enterprise AI best practices.
Retrieval Augmented Generation, or RAG, has moved from an experimental pattern to a board-level AI capability. In the prototype stage, RAG looks deceptively simple: connect a large language model to documents, retrieve a few relevant chunks, and generate a response with citations. At enterprise scale, the engineering problem changes. The system must enforce permissions, refresh data continuously, evaluate retrieval quality and grounding, manage latency and cost, support multiple data formats, resist adversarial prompts, provide observability, and prove measurable business value.
The timing matters. The latest Cisco AI Readiness Index 2025 shows that only 13% of organizations qualify as AI “Pacesetters,” yet those leaders are 4x more likely to move AI pilots into production and 50% more likely to report measurable AI value. At the same time, 82% of organizations say urgency to deliver return on AI has increased in the last 6 months. That urgency is exactly where production-grade RAG now sits. 1
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