← Anthropic Interview Insights
I started with a basic load balancer and stateless service layer, which felt right, but I fumbled when they pushed on how you'd handle token uniqueness at that throughput without a central bottleneck.
Start by clarifying requirements and constraints, then outline a high-level architecture that separates concerns (e.g., request handling, batching, model inference, and caching). Dive into scalability techniques like horizontal scaling, load balancing, and asynchronous processing, and discuss trade-offs between latency, throughput, and cost.
Pro tip: Emphasize the importance of batching and caching to reduce load on the model, and discuss how to handle failures gracefully with retries and circuit breakers. Also, mention the need for monitoring and autoscaling to adapt to traffic spikes.
Ask about expected latency, token length, model size, and budget constraints to scope the design appropriately.
Sketch a layered architecture: API gateway, load balancer, request queue, batching service, model inference cluster, and response cache.
Discuss horizontal scaling of stateless components, sharding, and using a distributed queue (e.g., Kafka) to decouple request ingestion from processing.
Cover dynamic batching, caching frequent prompts, model quantization, and using specialized hardware (GPUs/TPUs) to increase throughput.
Analyze trade-offs between latency and throughput, cost implications, and design for fault tolerance with retries, circuit breakers, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.