OpenAI System Design interview questions focus on architecting large-scale, production-grade systems that often integrate machine learning models, low-latency inference, and real-world operational constraints. What’s distinctive is the emphasis on LLM-specific trade-offs — GPU/compute constraints, batching and streaming behavior, caching and vector search for embeddings, safety and moderation hooks, and cost-versus-latency decisions — alongside standard distributed-systems concerns like reliability, scaling, and monitoring. Interviewers evaluate how you clarify ambiguous requirements, decompose a problem, choose sensible abstractions, justify trade-offs, and communicate trade-offs and failure modes clearly. Expect an open-ended prompt, a short clarification phase, a high-level architecture sketch, and a focused deep dive on one component; sessions are typically 45–60 minutes and reward iterative thinking and concrete numbers. For interview preparation, practice asking crisp clarifying questions, grounding designs in capacity and latency estimates, rehearsing model-serving and data-pipeline patterns, and walking through failure scenarios and observability. Use a whiteboard or drawing tool to explain flows, practice end-to-end examples that touch on security, billing, and reliability, and run mock interviews that force you to justify trade-offs under time pressure.