Walk Through an AI Project You Built: Decisions, Alternatives, Trade-offs, Impact

Pick one project you built in which AI, such as a machine learning model or a large language model, played a central role, and walk the interviewers through it in depth. The focus is how you used AI in the project. Cover: - the problem you were solving, and for whom; - what made it technically hard; - the architecture decisions you made; - the alternatives you considered; - the trade-offs you accepted and the lessons you learned; - the final business or customer impact. Expect detailed follow-up questions, especially on the alternatives and the trade-offs. ```hint Pick the project for its decisions Choose a project in which you made real choices between options you can still compare in detail, not simply the one with the biggest headline. ``` ```hint Treat AI as a decision Be ready to explain why AI was needed at all, which simpler approach you ruled out, and how you knew the model's output was good enough. ``` ### Clarifying Questions - Should the project be one where I personally owned the AI components, or can it be a team project where I led one part? - Do you want the whole story first and questions afterward, or should I expect interruptions as I go? - Some details are confidential. Is a description of the architecture with abstracted names and numbers acceptable? ### What a Strong Answer Covers - A crisp problem statement: the user, the constraint, and why AI was the right tool - Specific technical difficulty (data, evaluation, reliability of model output, latency, cost) and how it was overcome - Architecture decisions with at least two real alternatives each and the criteria used to choose - Honest trade-offs and lessons, including what you would do differently - Measured impact tied to a business or customer outcome, with how it was measured - Clear personal ownership at staff scope, including influence beyond your own code ### Follow-up Questions - Which alternative came closest to winning, and what evidence would have changed your decision? - How did you evaluate model quality before launch, and how did you catch regressions afterward? - Where did the AI component fail in production, and what guardrail did you add because of it? - If you rebuilt the system today with current models, what would you change in the architecture?