Verify before you trust
Three cheap checks that turn a fluent AI explanation into confirmed knowledge, and why you must understand a fix before calling it fixed.
An AI answer that reads as confident and well formatted is still just a hypothesis until you confirm it against the real system. The failure mode that burns people is accepting a fluent explanation, changing code to match it, and shipping a fix for a bug that was never actually there.
Three cheap checks turn a guess into knowledge:
Reproduce first. If you cannot make the bug happen on demand, you cannot know you fixed it. Ask the model to help you write a reliable reproduction, then run it yourself and watch it fail.
Confirm the mechanism. Do not just apply the fix. Add a log line or a breakpoint that proves the variable really held the value the model claimed. If the observed evidence does not match the story, the story is wrong, however well written it was.
Change one thing. When you apply a suggested fix, change only that, and watch the reproduction go green. If you changed five things at once, you do not know which one mattered, or whether you quietly introduced two new bugs.
The rule that ties it together: understand why the fix worked before you call it fixed. A green test with no understanding behind it is a bug in waiting, because the symptom can go quiet for reasons that have nothing to do with the real cause.
Take an AI explanation of a bug and, before changing any code, add one log line or assertion that would prove or disprove the claim. Run it and record what you saw.
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