Explain AI Tool Use, Model Selection, and Engineering Validation

Explain how you use AI tools in engineering work, how you choose models for different tasks, and how you verify that the tools are useful for their intended users. ### Requirements and Constraints Use actual experience or clearly label an example as hypothetical. Cover coding and at least one noncoding use if you have one. Do not invent a percentage of AI-written code, a model name, a company budget, or a policy you do not know. Clarify whether a described tool serves your own team or helps other teams understand and use your systems. ### Clarifying Questions - Was the tool or workflow requested by a manager, or did you initiate it to address a problem? - Who uses its output, and what decision or engineering task does that output support? - Which model was used for coding or summarization, and what evidence influenced that choice? - What token, subscription, or usage limits apply, and how are costs accounted for? ```hint Define what an AI contribution measure counts Generated lines, accepted suggestions, reviewed changes, and completed engineering tasks measure different things. A precise definition matters more than a confident percentage. ``` ### What a Strong Answer Covers - Concrete AI-supported tasks and the human responsibilities retained for requirements, review, and acceptance. - The intended users, original problem, and evidence that the workflow helps them. - Task-specific model-selection criteria, including output quality, latency, cost, and organizational constraints. - Honest treatment of unknown model details and uncertainty in estimates of AI contribution. - Validation of code or summaries, handling of unsupported claims, and appropriate data boundaries. - A cost and usage approach tied to useful outcomes rather than token consumption alone. ### Follow-up Questions 1. When would you use a different model for a summary than for a code change? 2. How would you establish whether a tool built for your team is understandable and safe for another team to use? 3. What would you report if you were asked for the fraction of AI-written code but had no reliable measurement?