This one tripped me up a little because I kept wanting to make it a story about the AI being wrong, but they seemed more interested in the process of deciding what even needed checking.
Use the STAR method to describe a specific instance where you verified AI-generated code or output. Focus on how you assessed risk to prioritize checks, the verification techniques you applied, and how you addressed any issues found. Highlight your decision-making process and the impact on the project.
Pro tip: Emphasize that you treat AI-generated output as a starting point, not a final solution, and that you always validate against requirements and edge cases. Show that you balance speed with quality by focusing verification efforts on high-risk areas.
Briefly describe the project, your role, and the AI tool used to generate output. Explain why verification was necessary (e.g., production impact, security, correctness).
Explain how you identified critical areas to verify, such as business logic, edge cases, security vulnerabilities, or performance. Mention any risk assessment or heuristics used.
Detail the specific techniques you used, such as unit tests, integration tests, code reviews, static analysis, or manual inspection. Explain why you chose those methods.
Describe any errors or issues found, how you fixed them, and whether you provided feedback to improve the AI or your process. Highlight collaboration with teammates if applicable.
Conclude with the outcome (e.g., successful deployment, avoided bug) and what you learned about verifying AI output for future projects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.