Frontend dev, 10+ yrs React. Creator of Evaficy Smart Test — AI-powered QA test management. Built to replace spreadsheets and manual test case writing.
The prompt quality pattern you're describing in Mistake 1 and 2 holds exactly the same way when using AI for QA test cases rather than unit tests. Vague input produces confident but wrong output - and the dangerous part is it looks complete.
The angle I'd add: with unit tests you can verify correctness programmatically - the test either passes or fails. With acceptance-criteria-based test cases, the validation is human - which means AI-generated gaps in coverage are much harder to catch. A missing negative scenario in a manual test case just never gets tested.
That asymmetry is what makes prompt engineering even more critical on the QA side. You can't rely on a failing test to tell you the AI missed something.
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The prompt quality pattern you're describing in Mistake 1 and 2 holds exactly the same way when using AI for QA test cases rather than unit tests. Vague input produces confident but wrong output - and the dangerous part is it looks complete.
The angle I'd add: with unit tests you can verify correctness programmatically - the test either passes or fails. With acceptance-criteria-based test cases, the validation is human - which means AI-generated gaps in coverage are much harder to catch. A missing negative scenario in a manual test case just never gets tested.
That asymmetry is what makes prompt engineering even more critical on the QA side. You can't rely on a failing test to tell you the AI missed something.