Using AI Assistants in Your Development Workflow: Verification, Testing and Judgment

In the behavioral portion of a software engineer loop at a cybersecurity company, the interviewers mixed questions about your past work with hypothetical workplace scenarios, and several of them focused on how you use AI tools in your development process. The exact wording was not reported; the two parts below are representative of that theme. The interviewers in this loop paid close attention to reasoning, testing and boundary conditions, so expect to be asked how you would check anything an AI tool produces. ### Clarifying Questions - Which AI tools, if any, are approved for use with company code, and are there rules about what code or data may be sent to them? - Is the question about day-to-day coding only, or also about code review, debugging and documentation? - In the scenario, who is waiting on the fix, and how firm is the deadline? ### Part 1 — How AI fits into your development process Describe how you currently use AI assistants when you write software. Where do they help you most, where do you avoid relying on them, and how do you make sure the code they produce is correct, including how you test it and which edge cases you check? ```hint Name your checks Think about what happens, step by step, between an AI suggestion appearing on your screen and that code being committed. ``` #### What This Part Should Cover - Specific tasks where AI helps, and tasks where you deliberately do not rely on it - A concrete verification routine: reading the code, testing it, checking boundary cases - Responsible handling of proprietary code, secrets and suggested dependencies - Ownership of the final code regardless of who or what wrote it ### Part 2 — A deadline scenario Imagine a fix is due today. An AI assistant suggests a change that makes the failing test pass, but you do not fully understand why it works. What do you do, and how do you communicate with your team if you might miss the deadline? ```hint Time-box and tell Consider how long you would spend building understanding before you stop and tell someone, and which options you would bring them. ``` #### What This Part Should Cover - Not merging code you cannot explain, while staying practical about time - How you would quickly build understanding and confidence, through tests, documentation or a colleague - Early, clear communication of the risk and the options when time is limited - When and how to ask for help ### What a Strong Answer Covers - Treats AI as an accelerator while keeping the engineer accountable for correctness - Testing and edge-case habits described concretely rather than in general terms - Awareness of security and confidentiality risks, fitting for a security-focused employer - Honest trade-off thinking between speed and quality under time pressure - Early, clear communication and a willingness to ask for help ### Follow-up Questions - Tell me about a time an AI suggestion was wrong. How did you catch it? - How would you review a teammate's pull request that was largely AI-generated? - An AI assistant recommends a third-party package you have never heard of. What do you check before adding it?