Every AI builder starts here: you try your product, it seems to work, and you conclude it works. This lesson is about why that conclusion is dangerous, and it is the doorway to everything else in the week.

The trap is that "it looked good when I tried it" is not evaluation, it is a vibe check, and vibes do not survive contact with change. The moment you tweak a prompt, swap a model, or add a feature, you have no way of knowing whether you made things better or quietly broke something, because you never actually measured how it was doing in the first place. An AI feature can look fine on the three inputs you happened to try and fail on the twenty you did not. It can sound confident and be wrong. Unlike ordinary software, which tends to fail the same way every time, an AI feature's behaviour varies, so a single good impression tells you very little.

The reason this matters so much is speed. Success with AI products, like success in software generally, comes down to how fast you can iterate with confidence, and you cannot iterate with confidence on something you cannot measure. Teams stuck in the vibe-check trap can build a demo but cannot improve it reliably, because every change is a guess. The way out is a real evaluation habit, and the rest of this session teaches you one, called TRACE. For now, just hold the discomfort of the trap clearly: if the only evidence your product works is that it felt fine when you tried it, you do not actually know that it works.