weber.st.michael
New member
When implementing unit ai workflows, the most common mistake is trusting the generated mocks without a manual sanity check. AI is great at boilerplate, but it can struggle with complex business logic that isn't explicitly defined in the function's context.
To close this gap, I’ve found that a Human-in-the-Loop (HITL) approach is mandatory. You let the AI handle the 80% (structure, mocks, happy paths), while you focus your expertise on the 20% (edge cases and security-critical paths).
This guide actually covers some great strategies for bridging that validation gap and ensuring your AI-generated tests are actually reliable:https://testomat.io/blog/ai-unit-testing-a-detailed-guide/
How are you guys handling the review process for AI-generated code in your pipelines? Are you using automated gatekeepers or manual code reviews?
To close this gap, I’ve found that a Human-in-the-Loop (HITL) approach is mandatory. You let the AI handle the 80% (structure, mocks, happy paths), while you focus your expertise on the 20% (edge cases and security-critical paths).
This guide actually covers some great strategies for bridging that validation gap and ensuring your AI-generated tests are actually reliable:https://testomat.io/blog/ai-unit-testing-a-detailed-guide/
How are you guys handling the review process for AI-generated code in your pipelines? Are you using automated gatekeepers or manual code reviews?