Topic overview
Study architecture, model serving, retrieval, queues, caching, observability, evals, feedback loops, and safety controls.
Core concepts
Latency, throughput, cost, fallbacks, rate limits, drift, monitoring, data governance, and human review.
Why it matters
Production AI fails at boundaries: unclear requirements, weak evaluations, poor observability, and unbounded model behavior.
Interview relevance
Interviewers want structured tradeoffs, clear assumptions, metrics, and a pragmatic rollout plan.