AI System Design Interview Questions: ChatGPT, RAG, LLM Inference, and Agents

Short summary

Modern AI system design interviews differ fundamentally from traditional distributed systems questions. This guide covers designing ChatGPT-like assistants, RAG systems, LLM inference platforms, and AI agents—requiring knowledge of new metrics (answer correctness, hallucination rates), GPU/memory constraints, token streaming, vector retrieval, and safety controls. The post structures interview preparation with clarification questions, estimation frameworks, and a component-level architecture breakdown showing how authentication, billing, retrieval, inference scheduling, and tool execution layers integrate in production AI systems.

• •Design questions now include ChatGPT, RAG, agents, and LLM inference—requiring deep knowledge of accelerators, vector retrieval, and safety controls
• •AI systems need new quality metrics: answer correctness, hallucination rates, retrieval quality, tool-use success—not just latency and availability
• •Production architecture requires careful handling of streaming (time-to-first-token), cost control, safety checks, and graceful degradation under load

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