"Walk me through how you'd take an AI feature from prototype to production."

The core system-design question for any AI/Applied Engineer role. Practice until you can deliver all five beats in under two minutes without listing tools — the interviewer is grading judgment, not vocabulary.

Miss one and they probe.

1. Define the system — real load numbers and what "quality failure" means for this feature
2. Stack the five layers — gateway → orchestration → LLM provider → cache/storage → observability
3. Name the tradeoff — where you sit on the latency/cost/quality triangle, and why
4. Reliability patterns — retry, timeout, circuit breaker, fallback, in that order
5. Observability — the specific metrics you'd alert on, not just "we'd monitor it"

"This feature handles 500 users/day, roughly 25K requests/month — quality bar is no hallucinated sources, so we need an eval feedback loop before launch. Stack: API gateway for auth/rate-limiting → orchestration layer for retries and tool calls → Anthropic as the LLM provider → Redis for exact-match caching plus pgvector for semantic search → Langfuse for observability. We sit closer to quality than cost, since wrong sources destroy trust — managed via model routing, small model for simple queries, larger model only for heavy tool-calling chains. Reliability: three retries with exponential backoff, a 10-second timeout, a circuit breaker that opens after five consecutive failures with a 30-second cooldown, and a static fallback message. We'd monitor P99 latency, error rate, cost per request, automated eval score, and cache hit rate — alerting the moment any of them drifts from baseline."

Swap in your own numbers and stack — the five beats, not the exact tools, are what's being graded.