Separate observations, hypotheses, and tests before asking AI to debug.
The move: separate what happened from what you think it means. AI is useful in debugging because it can hold several possible causes at once, compare them, and suggest tests. It becomes risky when you give it a conclusion disguised as a fact. "The cache is broken" sounds specific, but it is often an interpretation. "The same request returns stale data for account 184 after deploy 621" is evidence. The scientific habit is to test ideas against evidence. In AI-assisted debugging, that habit becomes a prompt structure: observed facts, candidate hypotheses, and discriminating tests. A discriminating test is not just…
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