# Discuss AI Use, Deadlines, Ambiguity, and Feedback Prepare evidence-based responses to the following behavioral themes. Use distinct examples where possible and state your own decisions and actions clearly. ### Part 1: Using Generative AI Describe how you use generative AI in engineering work and what you do when its output differs from your expectation. #### What This Part Should Cover - A concrete use case and success criterion - Diagnosis of whether the prompt, context, model, or expectation is wrong - Independent verification and a stopping rule - Security and privacy boundaries ### Part 2: Technical Complexity and Deadlines Describe a complex technical problem and a situation involving a tight or missed deadline. Explain prioritization, risk communication, and the outcome. #### What This Part Should Cover - Why the work was difficult - Early identification of schedule risk - Scope, sequencing, or resource trade-offs - Honest communication and learning from a miss ### Part 3: Ownership and Ambiguity Describe a time you acted beyond your formal responsibility and a time requirements were ambiguous. #### What This Part Should Cover - A reason to act that served the team rather than personal visibility - Stakeholder alignment and explicit assumptions - A reversible first step or experiment - Avoidance of unbounded ownership or hidden decisions ### Part 4: Critical Feedback and Rapid Learning Describe critical feedback you received and a situation where you had to learn something new quickly. #### What This Part Should Cover - The feedback stated fairly and without defensiveness - A concrete behavior change - A focused learning plan and credible sources of validation - Evidence that the change improved later work ### What a Strong Answer Covers Strong answers establish context briefly, focus on personal judgment and collaboration, use honest evidence, and include reflection. They do not turn every result into a flawless success; a well-examined miss can demonstrate stronger ownership than a vague win. ### Follow-up Questions - When did you stop iterating with an AI tool and solve the problem another way? - What did you cut to protect a deadline, and who agreed? - Which assumption in an ambiguous project proved wrong? - How did feedback change a later decision?