A RAG demo is easy: point an LLM at a vector store, ask it a question you already know the answer to, and watch it work. A RAG system you can put in front of customers is a different problem — one where the failure modes are quiet, the stakes are real, and "it worked when I tried it" tells you almost nothing about how it will behave in production.
Grounding is a data problem before it's a model problem
Most RAG quality issues trace back to retrieval, not generation. Chunking strategy, embedding freshness, and how well your index reflects the actual structure of your source documents matter more than which model sits on top. Before tuning prompts, we look hard at whether the right passage is even being retrieved — if it isn't, no amount of prompt engineering fixes the answer.
Evaluate against real tasks, not vibes
"It seems good" is not a metric. A production RAG system needs a golden set of real questions with known-correct answers, run automatically against every change to the pipeline — retrieval logic, prompt, or model version. We track groundedness (does the answer actually come from the retrieved context), relevance, and a hallucination rate, and we treat regressions on any of the three as a blocking issue, the same way you'd treat a failing test in CI.
Guardrails are what make it safe to ship
Citations that link back to source passages, a defined refusal behaviour for questions outside the system's scope, and explicit handling for PII in both the retrieved context and the model's output — these aren't nice-to-haves, they're what turns a clever prototype into something legal and security will actually sign off on.
Production is where the real learning happens
Once it's live, the job isn't done. Retrieval quality drifts as your source documents change. User questions evolve in ways your original golden set didn't anticipate. We instrument production traffic for exactly this: sampling real queries, flagging low-confidence answers for review, and feeding that signal back into the evaluation set on a regular cadence — so the system gets more reliable over time instead of quietly degrading.
None of this is about picking a fancier model. The teams that ship GenAI customers actually trust are the ones who treat grounding, evaluation and guardrails as first-class engineering work — with the same rigour they'd apply to any other production system.