Classic question but still hard to nail in 45 minutes.
Start by clarifying requirements and scope, then outline a high-level architecture covering the main components: client, API gateway, model serving, and data storage. Dive into key design decisions like model serving infrastructure, scaling, and latency optimization, and discuss trade-offs and potential bottlenecks.
Pro tip: Emphasize the importance of iterative development and monitoring, and discuss how you would handle model updates and A/B testing without disrupting service. Show awareness of cost and efficiency trade-offs in serving large language models.
Ask questions to understand expected scale, latency requirements, features (e.g., streaming, context length), and constraints. Define functional and non-functional requirements.
Sketch the main components: client applications, API gateway, load balancer, model inference service, conversation storage, and monitoring. Explain data flow from user input to response.
Discuss model serving infrastructure (e.g., GPU clusters, batching, caching), scaling strategies (horizontal scaling, auto-scaling), and latency optimization (model quantization, distillation).
Talk about trade-offs between cost, latency, and quality; handling long conversations; managing state; and ensuring reliability and fault tolerance.
Cover deployment strategies (canary, blue-green), monitoring, logging, and how to handle model updates and rollbacks without downtime.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.