AI System Design Casebook

A framework for the AI system design round, then cases worked end to end: requirements, architecture, tradeoffs, evals and failure modes.

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Who it’s for

For engineers with an AI or ML system design round coming up, at any level where the loop includes design.

You should know the building blocks (LLM APIs, retrieval, queues, caches). The casebook is about putting them together under questioning.

What it covers

The framework
Worked cases
Tradeoffs
Failure modes

A question from the guide

One of the 12 questions, with its model answer.

The framework

How do you clarify requirements for an LLM feature?

Model answer

I start with the user's task and what a failure costs. A wrong answer in an internal search tool is an annoyance; a wrong refund is lost money. Then I ask the questions that change the architecture: what data the model needs and who is allowed to see it, whether that data is private, changing or both, what latency the user will tolerate, the expected traffic, and a cost ceiling per request or per month.

I also ask whether the model only answers or can take actions, because actions bring permissions and approvals into scope. For a "chat with our handbook" feature, I'd write down: 5,000 employees, per-department access, answers with citations, first words on screen within a second or two. Those four facts already rule out stuffing every document into one prompt.

What they’re checking:: Whether you turn a vague product idea into the constraints that decide the design (data, permissions, latency, cost, side effects) before drawing boxes.
Likely follow-up:: Which of those requirements would change your design the most if the answer flipped?

See all 12 questions: AI System Design Interview: Framework and Practice Questions →

What the guide will include

A framework for the round
Requirements, architecture, tradeoffs, evaluation and failure modes, in the order you say them.
Worked designs
Prompts worked end to end the way you would answer them at the whiteboard, with the diagram.
Tradeoffs to name
The choices interviewers push on, and what each option costs in latency, money and risk.
Failure modes and evals
How the system breaks in production, how you would notice, and what you would measure.
A PDF you can print
Read it on your phone before the call, or print the sections you want to drill.

Planned contents

The guide isn’t out yet, so chapters and their order may still change.

1. The framework: requirements, architecture, tradeoffs, evals, failure modes
2. A RAG assistant over internal documents
3. A customer-support agent that can issue refunds
4. Cutting a feature’s inference cost
5. An AI code-review bot for a large repository
6. Semantic search for a product catalog
7. Document extraction with structured outputs at scale
8. Evals and monitoring for an LLM feature already in production
9. Making a slow chat assistant feel fast
10. Securing an agent that can read email and files
11. Self-hosting an open-weight model versus using an API

Free PDF

All 12 questions with model answers

A model answer for every question, what the interviewer is checking, the follow-up and the mistake to avoid.

Questions about the guide

Is the PDF really free?

Yes. Leave your email and we send the PDF the day it is ready. No payment details, and every email has a one-click unsubscribe link.

How is it different from the questions page?

The questions page lists every question with a handful of sample answers. The PDF has a model answer for each question, plus what the interviewer is checking, the likely follow-up and the mistake to avoid.

Is it based on a specific company’s interviews?

No. It is written from the concepts the role or system depends on. We don’t name companies or claim to know what any one company asks.

When will it be ready?

It isn’t out yet. Leave your email and we send it the day it is; nothing else arrives before then.