AI System Design

A framework for AI system design interviews

A repeatable structure for 45-minute LLM system design rounds, with what interviewers listen for at each step.

Latency, cost and reliability

Concrete levers for making LLM features fast, affordable and resilient in production.

Guardrails, safety and prompt injection

Input and output moderation, prompt-injection defence, and designing agents with least privilege.

Design a customer-support chatbot that answers from a company knowledge base.

How would you reduce the latency of an LLM feature that currently takes 8 seconds?

Your LLM bill doubled last month. How do you investigate and reduce it?

How do you get reliable JSON out of an LLM?

What is prompt injection and how do you defend against it?

Design a content moderation service that inspects LLM outputs in real time.

How would you evaluate a new model before switching production traffic to it?

When should you NOT use an LLM?

Explain semantic caching and its risks.

How would you design a model router?

What would you log for an LLM request, and what would you avoid logging?

What is the 'lethal trifecta' for AI agents?

Design an LLM inference service that serves open-weight models on your own GPUs to internal teams.

Design a multi-tenant platform where customers fine-tune and serve their own LoRA models.