How do you approach an open-ended AI system design question?

Treat it like any system-design round, then add the AI-specific layers. First clarify requirements: who uses it, expected traffic, latency budget, accuracy bar, and cost ceiling. Sketch the data flow — ingestion, retrieval or context building, the model call, and post-processing. Then reason explicitly about the three levers that dominate AI systems: quality (which model, grounding, evaluation), latency (caching, streaming, smaller models), and cost (token usage, batching, routing cheap-vs-expensive models). Call out failure modes — the model API can be slow, rate-limited, or wrong — and design fallbacks. Finish with how you'd measure success and catch regressions. Interviewers reward stated assumptions and trade-off reasoning far more than a 'perfect' diagram.