This is a deceptively open question that turns into five separate conversations if you're not careful.
Choose a project where AI assistance was integral but you maintained ownership of the architecture and quality. Structure your answer as a narrative: problem, AI-augmented workflow, key technical decisions (especially trade-offs), verification methods, and lessons learned. Emphasize how you leveraged LLMs to accelerate development while ensuring correctness and maintainability.
Pro tip: Be specific about how you validated AI-generated code—mention techniques like unit tests, integration tests, static analysis, and manual review. Also, discuss a concrete change you'd make, showing self-awareness and continuous improvement.
Briefly describe the project, the problem it solved, and why it was important. Mention the tech stack and your role.
Detail how you used LLMs in your workflow: for code generation, debugging, documentation, or design brainstorming. Give specific examples.
Discuss key architectural or implementation choices, including trade-offs (e.g., using an LLM for API integration vs. manual coding). Explain why you made those decisions.
Explain how you ensured correctness: testing strategies, code reviews, monitoring, and handling AI hallucinations or errors.
Share what you'd do differently next time, focusing on process improvements, tooling, or architectural changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.