AI can generate test cases quickly, but if you use it only as a generator, the output often becomes generic and misses the deeper context of the product.
That’s why I increasingly use AI as a reviewer rather than a creator.
My typical prompt looks something like this:
“Here is my test plan. Review it like a Senior QA. Identify missing edge cases, risky assumptions, negative scenarios, permission gaps, and regression risks. Don’t rewrite everything—just tell me what I may have missed.”
The quality of feedback is significantly better.
This approach forces me to apply my own critical thinking first and then use AI to challenge that thinking. If AI creates the first draft, it’s easy to become dependent on its suggestions. But when AI reviews my work, it pushes me to think more deeply and identify blind spots.
For me, AI is not here to replace QA thinking—it is here to pressure-test it.
A strong QA professional uses AI to ask questions such as:
- What am I missing?
- Where are the hidden risks?
- Which assumptions are weak?
- Which user roles have been overlooked?
- What negative scenarios have not been considered?
- Which regression areas could be affected?
- What workflow might silently break after this change?
The real value of AI in testing is not generating more test cases—it is helping us think more critically about the ones we already have.
Do you use AI more as a test case generator or as a QA reviewer?
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