Topic overview
Study end-to-end architecture for an AI feature, model serving strategy (hosted API vs. self-hosted), retrieval and caching layers, request queues, rate limits, fallback models, and safety controls at the system boundary.
Core concepts
Latency and throughput budgets, cost per request at scale, fallback chains when a primary model fails or is rate-limited, caching for repeated or similar requests, and data governance for what user data reaches which model or vendor.
Why it matters
Production AI fails at boundaries: unclear requirements, unbounded model behavior, and cost or latency that wasn't budgeted for before launch. Getting the architecture right up front is cheaper than retrofitting caching, fallbacks, or rate limits after an incident.
Interview relevance
Structured tradeoffs, clear assumptions, capacity estimates, and a pragmatic rollout plan matter more than a list of buzzwords.