GenAI Behavioral: Boosting Productivity, Learning New AI Tech, and Defending RAG/MCP Work

One round of the loop opened with two behavioral questions about generative AI before a low-level design exercise. The interviewer then followed up in depth on the projects behind the answers: an AWS migration, retrieval-augmented generation (RAG), the Model Context Protocol (MCP), the architecture design and the candidate's personal contribution. Answer the two questions, then be ready to defend the technical details of the work you describe. ### Clarifying Questions - Should the productivity answer be about AI tools in your own engineering work, or about AI features you built for users? - Are school, internship or side projects acceptable sources for the AI stories? - How deep should the architecture discussion go: components and data flow, or specific models, indexes and settings? ### Part 1 — Using generative AI to work more productively How have you used generative AI to improve your productivity? ```hint Measure it Pick one recurring task, and think about how long it took before and after, and how you checked that the AI's output was right. ``` #### What This Part Should Cover - Concrete tasks where AI was used, and the workflow around it - How the output was verified, and the guardrails you followed - A measured or credible impact, including where AI did not help ### Part 2 — Learning a new AI technology to solve a real problem Tell me about a time you learned a new AI technology and used it to solve a real problem. ```hint Learning path Think about what you learned first, what you built to test your understanding, and what result told you the approach worked. ``` #### What This Part Should Cover - The problem, and why the new technology was the right choice - How you learned it quickly and validated your understanding - The result in numbers, and what you would do differently ### Part 3 — Defending the technical details Expect the interviewer to dig into the systems behind your stories. For whichever apply, be ready to explain how a migration to AWS was planned, cut over and made reversible; how a RAG pipeline chunks, embeds, retrieves and is evaluated; what an MCP server exposes and how access to it is controlled; the overall architecture; and exactly which parts you built yourself. ```hint Draw it Sketch the components and the data flow on paper, mark the parts you personally built, and note one design choice per component that you could defend against an alternative. ``` #### What This Part Should Cover - A component-level architecture with its data flow and the reasons for key choices - How the AI components are evaluated, and how they fail - A clear boundary between your own contribution and the team's ### What a Strong Answer Covers - Specific, real uses of AI with verification built in and measured impact - A structured, fast learning story ending in a result that was evaluated, not just demonstrated - Technical depth on RAG, MCP or the migration that survives several follow-ups - Judgment about AI's limits, data confidentiality and correctness risks - Precise ownership of your part of the work ### Follow-up Questions - Your RAG system returns a confident answer that is wrong. How do you find out whether retrieval or generation failed? - How do you stop an MCP-connected assistant from taking an action the user did not intend, for example after a prompt injection hidden in a retrieved document? - When did AI make you slower or introduce a bug, and what did you change afterwards? - During the AWS migration, what was the rollback plan if the cutover failed?