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.
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AI System Design Interview Questions
Design a Conversational AI Customer Support System
Design an AI-powered customer support system that handles common queries automatically while escalating complex issues to human agents.
Read questionDesign a Document Q&A System for a Large Corpus
Design an AI system that answers natural language questions over a large collection of documents, with accurate citations and low hallucination rates.
Read questionHow Do You Estimate the Cost of Running a Production LLM System?
Walk through how to estimate and model the cost of running an LLM system in production — covering API token costs, open source GPU infra, and key levers for optimization.
Read questionDesign an AI-Powered Code Review System
Design a system that uses LLMs to automatically review pull requests — identifying bugs, style issues, and suggesting improvements at scale.
Read questionDesign a Real-Time Content Moderation Pipeline Using LLMs
Design a scalable content moderation system that uses LLMs to detect harmful content in real time while minimizing false positives and latency.
Read questionDesign a Production LLM Chat System (Design ChatGPT)
Walk through the architecture of a production LLM-powered chat system — covering streaming responses, conversation history management, context window limits, multi-user scaling, and safety.
Read questionHow Would You Architect a Multi-Model AI Gateway?
Design a unified gateway that routes requests across multiple LLM providers, handles fallbacks, enforces rate limits, and tracks costs per team.
Read questionHow Do You Architect a Multi-Tenant LLM Deployment with Role-Based Data Access?
Enterprise AI products serve multiple customers from shared infrastructure. Walk through how to design tenant isolation, role-based access control, and data governance for a multi-tenant LLM deployment.
Read questionPrep for the full interview loop
Know the concepts. Now prove it. Practice GenAI, Coding, System Design, and AI/ML Design interviews with an AI that tells you exactly where you fell short.
Start a mock interviewFrequently Asked Questions
What makes AI system design interviews different from regular system design?▾
AI system design adds probabilistic, non-deterministic components. Instead of services that return deterministic results, you're designing systems where outputs can vary, quality degrades over time, and failures are silent (wrong answers, not errors). This changes how you approach evaluation, monitoring, cost estimation, and tradeoffs — you need to reason about LLM-specific concerns like context window limits, token costs, hallucination rates, and latency.
What AI system design questions are most common in interviews?▾
Common AI system design questions: design a document Q&A system, design a customer support bot, design an LLM chat system at scale, design a RAG pipeline for enterprise search, design a content moderation pipeline, design an AI code review system, and estimate the cost and latency of an LLM-powered service. Each tests your ability to reason about the full stack from data ingestion to LLM inference to evaluation.
How should I structure my answer to an AI system design question?▾
Use this structure: (1) Clarify requirements — scale, latency targets, quality bar, cost constraints; (2) High-level architecture — identify the major components (ingestion, retrieval, generation, evaluation, monitoring); (3) Deep dive on the critical path — usually the retrieval + generation loop; (4) Tradeoffs — RAG vs fine-tuning, single model vs ensemble, online vs offline eval; (5) Failure modes — what breaks and how you'd detect and fix it.