Explain how you use AI tools to learn and code, and defend a past AI project

An interview with the hiring manager for an entry-level software engineer role opens with a block of questions about artificial intelligence. The interviewer is not testing machine learning theory. They want to hear how you actually work with AI tools, and whether your judgment about using AI holds up at the scale of a very large company. ### Clarifying Questions - Should I focus on AI coding assistants and chat tools, or also on projects where I built a feature on top of a model? - For the project, would you like an architecture walk-through, or mainly the decisions and results? - When you ask about scale, should I assume the tool serves one team or the whole organization? ### Part 1 — How you use AI to learn and to code Explain how you use AI tools when studying something new, and how you have built AI into your coding setup to get the most from it. Be specific about which tasks you hand to the tool, which you keep for yourself, and how you catch its mistakes. ```hint Be concrete Walk through one recent task step by step: what you asked the tool, what it produced, and what you changed before you used it. ``` #### What This Part Should Cover - Using AI to deepen understanding rather than to skip it - A concrete coding workflow with clear limits on what is delegated - How generated output is reviewed and tested, and kept within the employer's rules on code and data ### Part 2 — Defend a project that used AI Walk through a past project in which you used AI. The interviewer will press on three questions: was AI genuinely necessary, or would a simpler approach have worked; how did you, or how would you, verify that the AI's output is correct; and what would have to change for the project to serve an organization with a very large number of users and teams? ```hint Start from the baseline Before describing the model, say what the simplest non-AI solution would have been and how it compared. ``` #### What This Part Should Cover - An honest necessity check against a simpler baseline - A verification plan with measurable quality, not a few spot checks - The factors that change most at scale: cost, latency, reliability, data handling and ownership ### What a Strong Answer Covers - Specific examples rather than general enthusiasm about AI - Clear judgment about when AI adds value and when it only adds cost or risk - Ownership of output correctness, regardless of who or what produced it - Willingness to name the weaknesses of one's own project and how to fix them - Awareness of what operating at very large scale demands ### Follow-up Questions - Your tool's output was wrong in a way users did not notice for weeks. How would you have caught it sooner? - If the model provider's API were unavailable for an hour, what would your project do? - How would you choose between a hosted large model and a small model trained on your own labeled data? - What would you never let an AI assistant do in a production codebase, and why?