Frame AI tools as a productivity multiplier that you use selectively based on task complexity, risk, and your own expertise. Share concrete examples of where they've accelerated your work and where they've introduced subtle bugs or outdated patterns. Emphasize that you always review and test AI-generated code, and that you decide when to use them by weighing the cost of verification against the benefit of speed.
Pro tip: Mention that you treat AI suggestions as you would a code review from a junior engineer: helpful but requiring scrutiny. Also, highlight that you avoid using AI for security-sensitive or highly complex logic where subtle errors could be costly.
Explain how AI tools fit into your daily workflow, such as for boilerplate, documentation, or exploring unfamiliar APIs. Keep it concise and focus on the decision-making process.
Give 1-2 specific examples where AI tools saved you significant time or improved quality, like generating unit tests or refactoring repetitive code.
Discuss scenarios where AI let you down, such as producing incorrect logic, missing edge cases, or suggesting deprecated libraries. Be honest but not overly negative.
Outline how you decide when to lean on AI: consider factors like task criticality, your familiarity, potential for subtle bugs, and the overhead of verification.
Conclude by emphasizing that you remain accountable for the code, and that AI is just another tool that requires sound judgment and rigorous testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.