Before trusting an AI explanation, write its possible counterexample

A proposed reasoning exercise: before accepting an AI explanation, write one observation that would make you revise it. This is a hypothetical practice, not a measured learning result.

Suppose a fictional support team notices that unanswered requests often lack a project code. The assistant concludes: “Adding a project code will solve the delay.” The observation supports an association; it does not yet establish the proposed cause. Perhaps difficult requests are both slower and less likely to have a code.

Separate three lines:

• Observed: what the records actually show.
• Explanation: the mechanism you think could account for it.
• Revision condition: what evidence would weaken that explanation.

A useful counterexample might be a complete request with a valid code that still waits because nobody owns the next step. That would suggest examining routing, not declaring the code irrelevant.

Compare cases that differ in the suspected cause while recording other differences. Keep the conclusion as narrow as the evidence permits. A tiny exploratory comparison can help choose the next question; it cannot settle every cause.

What counterexample would change your mind about an explanation you currently find convincing?