# Explain an AI-Assisted Engineering Workflow You Can Defend Choose a recent engineering project that you personally worked on. Use it to explain the full path from an ambiguous problem to a maintained production system, with special attention to where AI tools helped and how you verified their output. ### Clarifying Questions to Ask - How much technical depth is appropriate for the audience? - Should the answer focus on an individual contribution, team process, or both? - May you discuss internal metrics in approximate or relative terms? ### Part 1: Frame the project and align requirements Describe the user or business problem, your scope, the stakeholders whose requirements differed, and how you turned incomplete information into an agreed design target. #### What This Part Should Cover - Clear ownership and project boundaries - Concrete requirement-discovery and alignment mechanisms - A decision or trade-off influenced by stakeholder input ### Part 2: Move from design to implementation Explain the design phase, the alternatives considered, how work was divided, and how the team kept implementation aligned with the approved design. #### What This Part Should Cover - Decision criteria rather than a technology list - Interfaces, milestones, or review checkpoints - Evidence that the implementation matched the intended outcome ### Part 3: Use AI without outsourcing judgment Walk through one concrete task you delegated to an AI agent. State what context and constraints you supplied, what the agent produced, and how you established that the result was correct before relying on it. #### What This Part Should Cover - A bounded task with a verifiable contract - Independent checks appropriate to the risk - An example of rejecting, correcting, or narrowing AI output ### Part 4: Operate and improve the system Explain what happened after launch: monitoring, maintenance, feedback, iteration, and one workflow change from the last year that you now rely on. #### What This Part Should Cover - Operational signals and ownership - A feedback loop tied to user or system outcomes - A durable process improvement, including its limits ### What a Strong Answer Covers - A coherent end-to-end narrative with the candidate's decisions clearly separated from team activity - Specific evidence, trade-offs, and verification instead of tool enthusiasm - Risk-sensitive AI usage, including where automation was deliberately avoided - Post-launch learning that changed later engineering behavior ### Follow-up Questions 1. What would make you stop using the AI-assisted approach for this class of task? 2. How did you prevent stale project context from degrading agent output? 3. Which verification step caught the most consequential problem? 4. What would you change if the same project had half the time or twice the risk?