Why Your AI Feature Works in the Demo and Breaks in Production

A founder I worked with showed me an AI feature that worked perfectly when he demoed it for investors, then broke about twice a week once real users got to it. Same code. Same model. He couldn’t tell me why it was so flaky, and neither could the developer who built it.

The problem turned out to be how they were pulling answers out of the model. The AI returned a paragraph of free-form text, and the code tried to fish the structured data it needed out of that paragraph by pattern-matching the words. Most of the time it worked. But every so often the model would open with a friendly sentence, or wrap its answer in a code block, or phrase a field a little differently, and the parsing fell apart. The feature never failed loudly. It just quietly handed the user garbage.

We changed one thing. Instead of asking the model for text and hoping, we constrained it to return JSON that matched an exact schema. The model is no longer allowed to answer outside that shape: no friendly preamble, no markdown wrapper, no creative reformatting. The parse failures went to zero.

That gap is the whole difference between an AI demo and an AI product. A demo has to work once, on stage, with you driving it. A product has to work ten thousand times for strangers while you’re asleep. Closing that gap is engineering discipline, not a smarter model.

So if you have an AI feature in production, ask your team one question: how do they get the data back out of the model? If the answer is parsing free-form text, you’ve got a flaky feature waiting to happen. If they’re using schema-constrained output, someone is paying attention to the part that actually breaks.

What’s the most unreliable thing you’ve shipped, and did you find it before your users did?

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