Approaching AI design questions
A repeatable structure for AI system design reviews and lead-level interviews.
Stable: this rarely changes. Reviewed 17 Oct 2026.
Key ideas
- 1. Clarify users, task, success metric, scale, latency and data sensitivity.
- 2. Start with the simplest baseline (single prompt or workflow) and say why.
- 3. Add retrieval, tools or agents only where the baseline fails.
- 4. Cover evals: how you measure quality before and after launch.
- 5. Cover operations: cost model, latency budget, observability, security, failure modes.
- 6. State trade-offs and what you would do next with more time.
Common mistakes
- Jumping straight to multi-agent architectures without a baseline or metrics.