RAG systems in production

Retrieval-augmented generation demos beautifully and breaks quietly. The gap between “it answered my test question” and “it’s reliable for real users” is where most enterprise AI projects stall. The retrieval half matters more than the model. If the right chunk of your data never makes it into the context window, no amount of prompt engineering will save the answer. That means investing in chunking that respects document structure, embeddings suited to your domain, and a retrieval step you can actually measure — recall on a real question set, not vibes. Evaluation is the part teams skip and the part that separates a toy from a product. You need a labelled set of questions and expected answers, a way to score responses, and a regression check that runs before every change. Without it, you’re shipping blind and every prompt tweak is a gamble. Then there’s safety in production: grounding answers in retrieved sources, refusing when confidence is low, and keeping a human in the loop for high-stakes decisions. We build AI features the same way we build everything else — measured against real outcomes, with guardrails and fallbacks — so they earn their place in the product instead of becoming a demo nobody trusts.