Behavioral: Deciding With Limited Data, a Process Built With AI, Spec-Driven Delivery

This behavioral segment of a technical phone screen asked three questions. Answer each one with a specific example from your own work, and use a different example for each question. ### Clarifying Questions - For the AI question, does the interviewer want an AI feature you shipped in a product, or a way you used AI to change how you or your team work? - For the third question, does "minimal manual effort" mean automating a recurring task, or driving a whole project from a written specification? The interviewer clarified this one; see Part 3. ### Part 1 — A decision made with limited data Tell me about a time when you had to make a decision with limited data. ```hint Name the gap Say exactly which information was missing, why you could not wait for it, and what a wrong call would have cost. ``` #### What This Part Should Cover - Why the decision could not wait, and what data was missing - How the risk was bounded: reversibility, guardrails, and a signal that would trigger a change of course - The outcome, how it was measured, and what later data showed ### Part 2 — A process implemented with AI Tell me about a time when you implemented a process with AI. ```hint A process, not a one-off Pick a repeatable workflow that you changed, and be ready to explain how anyone could tell when the AI step was wrong. ``` #### What This Part Should Cover - The process before and after, and why an AI step suited it - How the AI output was checked (human review, tests, evaluation on known cases), and what happened when it failed - Adoption by others and the measured impact - Risks handled: confidential data, incorrect output, over-reliance ### Part 3 — A project done with minimal manual effort Tell me about a project that you completed with minimal manual effort. When asked what this meant, the interviewer explained that they wanted to hear how you specified the project's requirements and then carried out the project using spec-driven development. ```hint Put the spec at the center Show what the specification contained, who reviewed it, and how the implementation and its checks were derived from it rather than written by hand. ``` #### What This Part Should Cover - How the requirements were captured as a reviewable spec: interfaces, acceptance criteria, edge cases, non-goals - How the implementation and tests were produced from the spec, including any code generation or AI tooling - How requirement changes and defects were handled through the spec - The result: effort saved, quality, and what still needed manual work ### What a Strong Answer Covers - Three distinct, specific, recent stories in which the candidate's own decisions are clear - A clear arc in each: context, the candidate's actions, measurable results, reflection - Judgment about risk and verification in every story, not only enthusiasm for speed or tools - Honest discussion of what went wrong or what would be done differently - Concise answers that leave the interviewer time to probe ### Follow-up Questions - In your limited-data decision, what evidence would have made you reverse course, and how would you have noticed it? - What did the AI step in your process get wrong, and how was it caught? - Partway through the spec-driven project, the spec turned out to be wrong. What did you change first, the spec or the code, and why? - What kind of project would you not run with spec-driven development?