AI Guides › Judgement & Guardrails

What To Do When You Catch AI Being Wrong

By Nigel Guy · 2 min read

You spot the mistake, feel a small flash of vindication for having checked, correct that one line, and carry on. It feels like the system worked — you caught it, after all. What usually doesn't happen next is any thought about why it was wrong, or whether the same reasoning produced other mistakes sitting quietly elsewhere in the same piece of work.

The mechanism: the catch sequence

1. Fix the specific error, but don't stop there. Correcting the one sentence you noticed is necessary and not sufficient.
2. Ask why it happened, specifically. A wrong date might be a stale training cutoff. A wrong figure might be a plausible-sounding invention. A wrong recommendation might be built on a misread instruction earlier in the conversation. Each of these has a different blast radius.
3. Check whether the same cause produced other errors. If the mistake came from a misunderstood instruction, everything downstream of that instruction is suspect, not just the one line where it surfaced visibly.
4. Re-verify the correction itself. An AI tool asked to fix a mistake will confidently produce a fix — that confidence carries exactly as little information as the original wrong answer did.
5. Log the pattern if it's the kind of mistake you might see again. Not formally — just enough that next time this tool or this kind of task comes up, you remember to look at that spot first.

What not to do next

Don't just say "no, that's wrong, try again" and accept the second answer with the same trust you gave the first — a corrected answer isn't independently more reliable than the one it replaced, unless you actually know what fixed it. Don't swing to the opposite extreme either and decide the whole tool is now untrustworthy for everything; one caught error tells you about that error's category, not about every category. And don't let the fact that you caught it this time quietly lower your guard for next time — catching a mistake is not evidence the next one will announce itself as clearly.

Guardrails

• Some errors are one-offs with no wider pattern. The point of checking scope isn't to assume the worst every time, it's to actually look rather than assume the best by default.
• If the mistake was consequential — money, a client-facing document, a decision already partly acted on — the check-for-scope step matters more than the fix itself. Fixing the visible symptom while the same error sits unnoticed elsewhere is the expensive version of this failure.
• This is about your own review habits, not a claim about how any particular tool is trained or how often it errs — that varies, and isn't the part worth building a habit around.

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