AI System Design

AI system design questions are increasingly common at top tech companies. They test whether you can reason about building production-grade AI systems — not just the ML model, but the entire architecture: data ingestion, model serving, monitoring, latency, cost, and failure modes.

Unlike traditional system design, AI system design adds new dimensions to reason about: model selection, prompt design, evaluation pipelines, feedback loops, and handling non-deterministic outputs.

In interviews, you'll typically be given a real-world AI product to design from scratch. The best candidates structure their answer using a framework: clarify requirements, define inputs and outputs, design the data pipeline, choose models and architectures, design the serving layer, and discuss evaluation and monitoring.