Trustworthy AI Testing
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Guide

How to Review AI-Generated Test Cases Before You Trust Them

AI tools can draft test cases in seconds, but they often look more complete than they are. As a QA engineer, your job is to verify, not rubber-stamp. Here's the 7-step review I use before any AI-drafted test work goes near sign-off.

  1. 1. Trace every test back to a requirement

    Check each case maps to a real acceptance criterion or user story. Flag tests that cover things nobody asked for, and requirements with no test at all.

  2. 2. Hunt for invented details

    AI happily makes up field names, error messages, endpoints and business rules. Check every specific value against the actual spec or application.

  3. 3. Check the negative and edge cases

    AI drafts tend to favour the happy path. Look for missing boundary values, invalid inputs, empty states, permissions and error handling.

  4. 4. Make sure expected results are testable

    “System works correctly” is not an expected result. Each step needs a clear, observable pass/fail outcome.

  5. 5. Remove duplicates and padding

    Look for cases that test the same thing with different wording. More tests does not mean better coverage.

  6. 6. Check for sensitive data

    Make sure no real customer data, credentials, secrets or internal URLs have been pasted into prompts or test data.

  7. 7. Record what you verified

    Keep a short evidence note of what you checked and changed, so the review stands up if anyone asks how the tests were produced.

Want the full checklist?

The Trustworthy AI Testing Kit includes a 15-point review checklist and 38 ready-to-use prompts for QA engineers.

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