Mastering Gen AI System Design Interviews

<p>This course contains the use of artificial intelligence. The course outline, the answer framework, the trade-off opinions, the mock-design answers and every benchmark number are my own, and every script is fact-checked against my published test articles before it is recorded.</p><p>A GenAI system design interview is not a classic system design interview with an AI box in the middle. The model is slow, expensive and does not give the same answer twice, and the interviewer wants to see how your design handles that. This course gives you one repeatable way to answer, and the model, retrieval, agent and production ideas you need to back it up.</p><p><span>What makes this course different</span></p><ul><li><p>One six-step answer framework you use in every lecture and every mock design: requirements, metrics, high-level design, deep dive, trade-offs and evaluation.</p></li><li><p>The model internals interviewers actually ask about: prefill and decode, attention, the KV cache, GQA, FlashAttention, PagedAttention, batching, speculative decoding and mixture of experts. Each one is taught for what it does to memory, speed and cost. No derivations.</p></li><li><p>Real measurements from my own machines, an RTX 5090 desktop and a MacBook Pro M5 Max, shown on the published source page. For example, a small draft helper took the same model from 73.6 to 133.6 tokens per second on the RTX 5090, and doubling the context from 128K to 256K made the wait for the first word 9.7 times longer, not 2 times.</p></li><li><p>Real failure stories turned into design lessons: the support bot that promised a wrong refund, the bill that blew up overnight, the AI provider that went down.</p></li><li><p>Four full mock interviews, each walked through all six steps: a customer support chatbot, a company document Q&amp;A system, a coding assistant and an agent that takes actions.</p></li><li><p>Short lectures of about 3 to 4 minutes. Each one ends with a sample sentence you can say in the interview room.</p></li></ul><p><span>What you will cover</span></p><ul><li><p>The interview and the framework: what is being tested, the six steps, quick estimates, the vocabulary, the five mistakes that sink candidates, and how to practise mock interviews out loud.</p></li><li><p>Inside the model: how a request is served, attention, the KV cache and how to shrink it, FlashAttention and PagedAttention, batching, speculative decoding, mixture of experts, sampling, structured output, picking a model, caching.</p></li><li><p>RAG systems in depth: RAG vs fine-tuning, embeddings and vector databases, chunking, query rewriting, hybrid search, reranking, citations, conversation memory, access control, evaluation and failure modes.</p></li><li><p>Agents and tool use: the agent loop, tool calling and MCP, context engineering, loops vs graphs, multi-agent systems, evaluating agents, guardrails on actions.</p></li><li><p>System 1 and System 2: fast decision models in front of the LLM, what a System One model returns (Jev), the open weights option (Laya), routing, guards and checks, and the System 1 interview playbook.</p></li><li><p>Production concerns: cost control, latency budgets, reliability, security, observability, evaluation and monitoring, safe rollouts, privacy.</p></li><li><p>Self-hosting and GPU sizing: GPUs for N users, quantization, inference servers, long context, API vs your own GPUs, splitting a model across GPUs.</p></li><li><p>Mock interviews: four complete designs, the follow-up questions interviewers love and a one-page interview checklist.</p></li></ul><p>Each of the 8 teaching sections ends with a short quiz and a downloadable cheat sheet, and a 30-question practice test with model answers closes the course.</p><p>By the end of this course, you will be able to take any open GenAI design question, ask the right first questions, put numbers on it, draw a design that holds up, and defend every trade-off out loud.</p><p>This is a theory course. There is no coding, and you do not need a GPU. If you know basic system design and have seen an LLM API, you are ready.</p>

What you'll learn:

• Answer any GenAI system design question with one six-step framework: requirements, metrics, design, deep dive, trade-offs, evaluation
• Estimate tokens, requests per second, daily cost and GPU memory for an LLM system in a few minutes on a whiteboard
• Explain how a model serves a request, from prefill and decode to the KV cache, and what each step costs in memory and speed
• Explain attention, GQA, FlashAttention, PagedAttention, batching, speculative decoding and mixture of experts in interview terms
• Design a RAG system end to end: chunking, embeddings, hybrid search, reranking, citations, access control and evaluation
• Choose between RAG and fine-tuning, and defend the choice with cost, freshness and behaviour trade-offs
• Design agents with tool calling, MCP, context engineering and guardrails on actions, plus a plan to evaluate them
• Plan for production: cost control, latency budgets, fallbacks, prompt injection, tracing, monitoring and safe rollouts
• Size self-hosted GPUs for N users, using memory for capacity and bandwidth for speed, and compare API cost with your own GPUs
• Walk through four complete mock designs: support chatbot, document Q&A, coding assistant and an agent that takes actions