How you use AI to work faster and raise your team's efficiency

Every round of this infrastructure loop included the same question: how do you use AI tools to work more efficiently day to day, and have you done anything to improve your team's efficiency? Answer with concrete examples from your own work. ### Clarifying Questions - Are you interested in coding work only, or also in operations work such as incident response, configuration and documentation? - Should team efficiency examples involve AI, or does any change that made the team faster count? ### Part 1 — How you use AI in your own work Which AI tools do you use, for which tasks, and how do you check what they produce? Pick concrete examples and say what changed as a result. ```hint Before, after, and a miss Choose examples where you can say what the task cost before and after, and one where the tool was wrong and how you caught it. ``` #### What This Part Should Cover - Specific tasks and the tools used for them - Verification habits and data-handling safeguards - Measurable impact on your own work ### Part 2 — Improving your team's efficiency Have you done anything that made your whole team more efficient? What did you do, how did you get others to adopt it, and how did you know it worked? ```hint Beyond your own desk Pick one change you drove that other people adopted, and be ready to show both adoption and a before-and-after signal. ``` #### What This Part Should Cover - Identifying a team-level bottleneck worth fixing - Driving adoption, including skeptics and any approvals needed - Measuring the outcome and guarding against new risks ### What a Strong Answer Covers - Concrete, recent examples rather than general enthusiasm for AI - Judgment about where AI helps and where it does not, with verification habits - Attention to code quality, security and the handling of sensitive data - Impact beyond yourself, with evidence of adoption and results - Examples that fit infrastructure work, such as configuration, scripting or incident triage ### Follow-up Questions - Tell me about a time an AI tool gave you a wrong answer that almost reached production. What did you change afterwards? - How do you decide which code or data is safe to share with an external AI service? - How would you tell whether a team-wide AI tool actually improves productivity rather than just increasing output?