Start by clarifying requirements and scope, then propose a high-level architecture that covers the end-to-end flow from user input to response generation. Focus on scalability, low latency, and safety, and be prepared to dive deep into specific components like model serving, caching, and rate limiting.
Pro tip: Emphasize trade-offs between latency, cost, and quality, and discuss how you would monitor and iterate on the system using A/B testing and user feedback.
Ask questions to understand expected scale (e.g., users, QPS), latency requirements, safety and compliance needs, and budget constraints.
Sketch the main components: client, API gateway, load balancer, inference service, model servers, cache, database, and monitoring. Explain how they interact.
Discuss model serving (e.g., GPU/TPU clusters, batching, quantization), caching strategies (e.g., semantic caching), and rate limiting/authentication.
Explain how to scale horizontally, handle failures (e.g., retries, circuit breakers), and ensure high availability across regions.
Describe content moderation, abuse detection, logging, and metrics for performance and quality, and how to iterate using feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.