Leading an AI-Powered Feature From Problem Discovery Through Launch

Choose an AI-powered feature that you shipped and take the interviewer through it from beginning to end: where the idea came from, how it was built and evaluated, how it launched, and what happened afterwards. ```hint Show the choices only AI forces Interviewers expect the usual product story, but they listen hardest for decisions that exist only when a model is involved: why AI was the right tool, how you judged output quality, and what you did when it was wrong. ``` ### Clarifying Questions - Does any machine-learning feature count, such as ranking or recommendations, or should it use a generative model? - Should the story focus on my own decisions as the PM, or also on how engineering and data science built it? - Is a feature that launched and was later changed or rolled back acceptable if the lessons are clear? - How much technical detail is useful: model choice and evaluation method, or mainly product decisions? ### What a Strong Answer Covers - A user problem and business goal that justified AI over a simpler rule-based or manual approach. - The candidate's own decisions across discovery, data, model or vendor choice, evaluation, launch and iteration, with the trade-off made at each step. - How output quality, safety and failure cases were defined and measured, before and after launch. - The launch approach and results tied to metrics, including what changed once real users arrived. - Honest reflection on what the candidate would do differently when building with AI. ### Follow-up Questions - What was the hardest trade-off between model quality, latency and cost, and how did you decide it? - Tell me about an output the feature got badly wrong after launch. How did you find out, and what did you change? - If you rebuilt the feature today with newer models, what would you do differently?