Grokking the AI System Design Interview

Why this round matters now

Design interviews increasingly include AI systems. You still need the usual building blocks (load balancing, caching, storage), but you also need to reason about training versus serving, embeddings and retrieval, inference costs that grow with usage, outputs that are not deterministic, and how to evaluate a system with no single correct answer. This course is built around exactly those questions.

What the course covers

Nine chapters and 70 lessons, each chapter ending with a capstone problem:

1. The AI interview landscape: how AI design questions appear, the three families of questions, and a framework for structuring answers.
2. ML systems foundations: training versus serving, training data, feature stores, batch versus online inference, embeddings and two-tower retrieval, model serving, offline evaluation, monitoring and drift, and A/B testing. Capstone: an ML platform for ETA prediction.
3. Classic ML design questions: recommendations, feed ranking, search ranking, ad click prediction, fraud detection and harmful content detection. Capstone: People You May Know.
4. LLM serving and inference: inference economics, back-of-the-envelope maths, serving optimisations, choosing between prompting, fine-tuning and RAG, and evaluating LLM systems. Capstone: the inference bill.
5. RAG and retrieval: vector databases, chunking and indexing, the RAG pipeline, hybrid search and reranking, and keeping answers grounded. Capstone: enterprise document Q&A.
6. Generative AI product designs: a ChatGPT-style assistant, a code assistant, AI-powered search, a text-to-image service, and safety and guardrails.
7. Agentic systems: what an agent is, tool use and function calling, orchestration and multi-agent systems, memory and context, and evaluating and controlling agents. Capstone: a customer-support agent.
8. Case studies: music discovery, harmful content at platform scale, visual search for a marketplace, a legal research assistant, an AI meeting assistant, text-to-image at scale, an autonomous coding agent, and critiquing a design.
9. Rapid review: rapid-fire questions, answers that sound junior, and decision cheat sheets.

Format, length and cost

It is text-based and self-paced with extensive assessments. The stated study time is about 20 hours. As of September 2026 the course page lists a one-time price of $123 with lifetime access, a completion certificate, a 4.6 out of 5 rating from 661 ratings, and 3,192 learners.

Before you start

The course page puts it simply: if you can write code and know what a model, an API and a database are, you are ready. No formal machine learning background is required. Familiarity with standard system design helps you move faster through the infrastructure parts.

How to get the most out of it

• Learn the framework first. Chapter 1's seven-step approach is what you will reuse in every case study, so practise it until it is automatic.
• Do the capstones out loud. Set a 45-minute timer, talk through the design as if an interviewer were listening, then compare with the course's answer.
• Always do the numbers. The inference economics and back-of-the-envelope lessons are what make an answer sound senior: estimate tokens, requests and cost before choosing an architecture.
• Use the rapid review chapter the week before an interview. The decision cheat sheets and "red flags that sound junior" are designed for last-minute revision.
• Plan on three to five weeks at a few hours a week, finishing with timed practice.

What you can do afterwards

You will be able to structure any AI design question, design classic ML systems and LLM-based products, explain RAG and agent architectures with their failure modes (training and serving skew, hallucination, prompt injection), and close an answer with trade-offs and a monitoring plan. The AI Mock Interviews in the same library are a natural way to test this under realistic conditions.

Common questions

Do I need machine learning experience? No formal background is needed, though you should be comfortable with code and basic system concepts.

Does it cover LLMs and agents or only classic ML? Both. Classic ML systems, LLM serving, RAG, generative AI products and agents each have dedicated chapters.

How long does it take? About 20 hours of study.

Best for: engineers preparing for design rounds at companies building AI products, and anyone designing an AI-backed system for the first time.