"In my Baskin-Robbins project, we had to test how menu item availability synced to the ordering system. I gave the AI a detailed prompt explaining the application and asked it to generate edge cases. It came up with several scenarios I hadn't considered - including one where an item marked as "available" in the app could actually be out of stock in the store."
"Prompt engineering is an extension of what we already do best: asking good questions. If you give AI a vague prompt, you'll usually get a vague answer, Surti said. If you apply the same discipline testers bring to writing test cases, you get results that are much closer to what testers actually need: clear, relevant, and actionable test scenarios they can trust."
AI is becoming a core QA tool that accelerates scenario generation, risk detection, and test planning while augmenting testers' capabilities. Effective prompting — using clear role definition, relevant system context, and explicit output format — produces clearer, more actionable test scenarios. AI can surface unexpected edge cases and prevent costly bugs, but human judgment remains essential to validate relevance and quality. Prompt engineering applies the same discipline as writing test cases and requirements. A simple prompt structure includes: define the role, add feature and system context, list rules or constraints, and specify the desired output format.
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