Planning the design of your production-grade RAG system

Planning the design of your production-grade RAG system The myth of "simple" RAG The retrieval gap revisited Why "advanced RAG" has emerged From pipelines to decisions Operational reality of RAG The hard boundary RAG cannot cross Why does this naturally lead to tuning Where this leaves us Red Hat AI Inference Server | Product Trial About the authors Frank La Vigne Robbie Jerrom More like this Enable intelligent insights with Red Hat Satellite MCP Server AI quickstart: Protecting inference with F5 Distributed Cloud and Red Hat AI Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share In our previous article Context as architecture: A practical look at retrieval-augmented generation , we treated retrieval-augmented generation (RAG) as an architectural idea. We explored why retrieval exists, how it changes the system around a language model, and where its boundaries lie. That framing is necessary, but incomplete. Once teams move beyond prototypes and begin operating RAG systems in production, a new reality sets in. Retrieval does not fail loudly. It fails subtly, probabilistically, and often convincingly. Systems return an answer, grounded in some source, even when that source is incomplete, outdated, or only loosely relevant. This is the point where RAG stops being an idea and becomes a systems problem. At a conceptual level, RAG looks straightforward: Store documents, retrieve relevant passages, pass them to the model. Many early implementations follow exactly this pattern—and appear to work. Until they don’t. The first failures are rarely catastrophic.