Most AI demos die on contact with reality. They look magical in a controlled prompt and fall apart the moment real users, real data, and real edge cases arrive. The gap between demo and product is not the model — it's the system around it.
Start with evaluation. Before you tune a single prompt, build a golden dataset of inputs and expected behaviors. Every change should move a number you can see. Without evals you are flying blind, shipping vibes instead of quality.
Then ground everything. Retrieval-augmented generation, structured outputs, and tight context windows keep models honest. Hallucination is rarely a model problem — it's a grounding problem.
Finally, instrument it. Tracing, cost monitoring, and feedback capture turn a black box into a system you can operate and improve. The teams that win with AI are the ones that treat it like software, not magic.