Behavioral: Verifying AI Output, Team-Wide AI Wins, and Investigating AI Errors

The behavioral part of a software engineering phone screen focuses entirely on how you use AI tools in your work. Answer each of the three questions with a specific example from your own experience. ### Clarifying Questions - Does "AI" here mean coding assistants only, or also AI models and features the team builds or runs? - For the team-benefit question, does an internal tool or a process change count, or does it need to be a shipped product feature? - Can the examples come from personal or open-source projects if AI tools were restricted at work? ### Part 1 — Making sure AI output is correct How do you make sure that what an AI tool generates for you is correct? ```hint Before, during and after Think about what you do before you accept generated output, which automated checks catch its mistakes, and what protects you after it ships. ``` ```hint Not every change deserves the same scrutiny Consider how your checks differ between a one-off script and a change to a production data path. ``` #### What This Part Should Cover - A concrete, repeatable verification workflow, illustrated with a real example - Evidence-based checks such as tests, type checks, reproduction and review, rather than reading the output and trusting it - Verification effort scaled to risk and blast radius - Clear ownership: the engineer is accountable for AI-generated work as if it were their own ### Part 2 — Using AI for something the whole team benefits from Have you used AI to build something that benefited your whole team? Walk me through it. ```hint From personal speed-up to team asset Pick an example where something you built with AI was adopted by others, not one that only made you faster. ``` ```hint Prove the benefit Be ready to say who used it, what it replaced, and how you know it helped. ``` #### What This Part Should Cover - The team problem and why AI was a good fit for it - What the candidate built and their individual role - Adoption and measured impact - Guardrails for quality, cost, sensitive data and long-term maintenance ### Part 3 — Finding that an AI result was wrong Tell me about a time you found that something an AI tool generated was wrong, and you investigated it. ```hint Start from the signal Begin with what made you suspicious, such as a failing test, an odd number or a reviewer's question. ``` ```hint Close the loop Think about what you changed afterwards so that the same kind of error would be caught earlier. ``` #### What This Part Should Cover - How the error was detected, and how long it had gone unnoticed - A structured investigation: reproduce, isolate, find the root cause - The fix, plus an assessment of whether anything wrong had already reached users or data - A process or tooling change that prevents a repeat ### What a Strong Answer Covers - Three distinct, specific examples rather than one story stretched across all three questions - A calibrated stance on AI tools: productive use combined with healthy skepticism - Results backed by numbers or observable outcomes - First-person ownership of everything shipped with AI help - Awareness of typical AI failure modes, such as invented APIs, subtle logic errors, outdated library usage and leaks of sensitive data ### Follow-up Questions - What kinds of tasks have you stopped giving to AI tools, and why? - A teammate merged AI-generated code that caused an incident. How would you handle the review and the follow-up? - How would you measure whether AI tools are making your team faster without lowering quality? - What information would you never paste into an external AI tool?