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Mastering Gen AI System Design Interviews

Name: Mastering Gen AI System Design Interviews
Rating: 5.0 (30 reviews)

Design LLM apps the way interviewers expect: model internals, RAG, agents, GPUs, cost and safety, 4 full mock designs

Created byLaxmi Kant | KGP Talkie

Last updated 10/2026

English

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

Course content

10 sections • 75 lectures • 4h 27m total length

Welcome to the Course1:49
Who I am, what the interview really tests and what this course gives you.
How to Get the Most Out of This Course1:34
How the lectures, captions, cheat sheets, quizzes and practice test fit together.
A Short Request, and Where to Find Me0:48
A review request within Udemy's rules, the bonus lecture and the Q&A.

What a GenAI System Design Interview Really Tests3:25
How it differs from classic system design, and why the model being slow, costly and non-deterministic is what you are graded on.
The Six-Step Answer Framework3:37
Requirements, metrics, high-level design, deep dive, trade-offs and evaluation, the structure used in every later lecture and every mock.
Quick Estimates for LLM Systems3:40
Tokens per request, requests per second, cost per day and GPU memory, worked through one example and checked against a real GPU.
The Words Interviewers Expect You to Use3:37
Prefill, decode, time to first token, throughput, grounding, recall, guardrails and eval set, one plain line each.
Five Mistakes That Sink Most Candidates3:31
Jumping to a model name, no metrics, no evaluation plan, ignoring cost and no failure handling.
Mock Interview Practice: AI Interviewers and Peer Mocks3:02
How to practise out loud before the real interview: timed AI mock interviews on HackerRank, live peer mocks on Aced Practice, and SideSense Pro's Coach mode, combined into one practice loop.
Quiz: The interview and the framework

How a Model Serves a Request3:35
Reading the prompt (prefill), then writing one token at a time (decode), and why answers stream word by word.
Attention in Plain Words3:42
Every token looks at every earlier token; queries, keys and values as a lookup; why long prompts get expensive.
Multi-Head Attention3:33
Several heads looking for different things at once, and what each head adds to compute and to the KV cache.
Shrinking the KV Cache: MQA, GQA and Hybrid Layers3:32
Sharing keys and values across heads, keeping a cache in only some layers, and why that decides how many users fit on one GPU.
The KV Cache, Why Chat Memory Costs GPU Memory3:48
How the cache grows with context and with users, and what compressing it to 4-bit saves.
FlashAttention and PagedAttention3:55
Computing attention in small tiles, and storing the KV cache in pages like an operating system so memory is not wasted.
Batching and Throughput3:34
Serving a million users from a few GPUs, and continuous batching in plain words.
Speculative Decoding3:44
A small helper drafts a few tokens and the big model checks them in one pass: same answers, faster.
Mixture of Experts3:33
A huge model that uses only a small part of itself for each token, and what that means for memory and speed.
Sampling and Temperature3:37
Why the same question gets different answers, and when to turn the randomness down.
Structured Output3:36
Getting reliable JSON from a model with schemas, validation and retries.
Picking a Model3:33
Hosted API vs open weights vs fine-tuned, compared on cost, latency, privacy and control.
Caching3:34
Prompt cache vs semantic cache, hit rate and the risk of stale answers.
Quiz: Inside the model

RAG vs Fine-Tuning3:39
The decision every interviewer asks about: knowledge vs behaviour.
Embeddings and Vector Databases3:38
What gets stored, how similarity search works, and index choices at a high level.
Ingestion and Chunking3:45
Parsing documents, choosing chunk size and overlap, and the metadata you keep.
Query Rewriting and Multi-Step Retrieval3:33
Fixing vague or follow-up questions before searching, and searching again when the first pass is not enough.
Hybrid Search3:33
Keyword plus vector search, and why each one misses what the other finds.
Reranking3:37
A fast first pass over many candidates, a careful second pass over the top few, and what that second pass costs.
Grounding and Citations3:28
Making the model answer only from retrieved text and show its sources.
Conversation Memory3:34
How a chatbot remembers earlier messages: a window, a summary or retrieval memory.
Access Control in RAG3:43
Users only see documents they are allowed to see, by filtering before retrieval, not after.
Evaluating RAG3:40
Retrieval metrics such as recall at k, and answer metrics such as faithfulness and relevance.
RAG Failure Modes3:29
Wrong chunk, stale data and conflicting sources, and the fix for each.
Quiz: RAG systems in depth

The Agent Loop3:26
Think, act, observe, repeat, and when a fixed pipeline is the better choice.
Tool Calling and MCP3:34
How a model asks for a tool, and how MCP gives every tool the same interface.
Context Engineering3:46
Choosing what the model sees at each step: instructions, tools, retrieved data and history.
Loops vs Graphs3:40
Letting the agent pick its route vs fixing the valid paths and checks yourself.
Multi-Agent Systems3:38
When splitting the work across agents helps, and when it only adds failure points.
Evaluating Agents3:31
Did the task succeed, which steps did it take, and what did each step cost.
Guardrails on Actions3:35
The bot that promised a wrong refund, and the policy checks, human approval and limits that stop it.
Quiz: Agents and tool use

Two Speeds of Thinking3:39
Kahneman's System 1 and System 2, why most steps in an AI app are fast decisions, and why a decision layer in front of the LLM helps cost, latency and reliability.
What a System One Model Returns3:42
Jev's choice, score and yes/no answers with probabilities and confidence, many questions in one parallel call, and why type safe is not the same as correct.
Laya, the Open Weights Option3:46
An Apache 2.0 System 1 model you can run yourself, and how to choose between Laya and Jev by deployment, option count, language and cost.
Route with System 1, Answer with System 23:40
Intent routing and triage for the support bot, confidence zones, version pinning and the fallback to the LLM or a human.
Guards and Checks with System 13:40
Input guards, passage filters and output checks as yes or no questions, and where they replace an LLM judge.
The System 1 Interview Playbook3:42
When to add the layer, evaluation per label, calibration and thresholds, and the failure modes to name.
Quiz: System 1 and System 2

Cost Control3:43
The bill that blew up overnight: rate limits, budgets, model routing, and why some languages cost more tokens.
Latency Budgets3:27
Time to first token vs total time, and the levers: streaming, smaller models, caching and the draft helper.
Reliability3:40
When the AI provider goes down: retries, a second provider, graceful degradation, and failures that throw no error.
Security3:49
Prompt injection and data leaks, input and output filtering, and least-privilege tools.
Observability and Tracing3:49
Logging each step's prompt, tokens, latency and cost so a slow or wrong answer can be traced.
Evaluation and Monitoring3:36
Offline eval sets, LLM-as-judge, online metrics and feedback loops, and comparing like with like.
Safe Rollouts3:32
Prompt and model versions, A/B tests and rolling back a bad prompt.
Privacy and Compliance Basics3:46
Handling personal data, data retention and where the model runs.
Quiz: Production concerns

How Many GPUs for N Users3:55
Memory sets capacity, bandwidth sets speed, worked from a real context sweep on an RTX 5090.
Picking a Quantization4:03
Bits per weight vs quality vs memory, and why a bigger model at fewer bits can win.
Inference Servers3:56
llama.cpp, Ollama and vLLM in plain words, what batching and paging buy you, and fair comparisons.
Long Context in Production4:03
1.4 seconds at 4K vs about 12.5 minutes at 256K on the same card, and when to retrieve instead.
API vs Your Own GPUs3:41
Finding the cost break-even, with prices stated as dated assumptions.
Beyond One GPU3:54
Splitting a model across GPUs with tensor and pipeline parallelism, at a high level.
Quiz: Self-hosting and GPU sizing

Mock 1: Customer Support Chatbot, Requirements and Estimates3:34
Steps 1 and 2 of the framework for an online store's support bot.
Mock 1: Customer Support Chatbot, Design and Deep Dive3:44
Steps 3 and 4: the high-level design and the parts the interviewer will probe.
Mock 1: Customer Support Chatbot, Trade-Offs and Evaluation3:41
Steps 5 and 6: the trade-offs, the evaluation plan and the failure handling.
Mock 2: Company Document Q&A, Requirements and Estimates3:48
Steps 1 and 2 for a Q&A system over internal documents.
Mock 2: Company Document Q&A, Design and Deep Dive3:42
Steps 3 and 4: retrieval, permissions and citations in one design.
Mock 2: Company Document Q&A, Trade-Offs and Evaluation3:55
Steps 5 and 6: the trade-offs, the evaluation plan and the failure handling.
Mock 3: Coding Assistant, Requirements and Estimates3:50
Steps 1 and 2 for an assistant inside a code editor.
Mock 3: Coding Assistant, Design and Deep Dive3:40
Steps 3 and 4: context from the codebase, latency and the model choice.
Mock 3: Coding Assistant, Trade-Offs and Evaluation3:58
Steps 5 and 6: the trade-offs, the evaluation plan and the failure handling.
Mock 4: An Agent That Takes Actions, Requirements and Estimates3:45
Steps 1 and 2 for an agent that makes bookings and refunds.
Mock 4: An Agent That Takes Actions, Design and Deep Dive3:42
Steps 3 and 4: tools, approval steps and limits.
Mock 4: An Agent That Takes Actions, Trade-Offs and Evaluation3:45
Steps 5 and 6: the trade-offs, the evaluation plan and the failure handling.
Follow-Up Questions Interviewers Love3:48
The questions that come after your design, and how to answer each one.
Your Interview-Room Checklist3:45
The framework on one page, plus the questions to ask the interviewer.
Quiz: Mock interviews
Practice test: 30 design questions with model answers

Bonus: What Next, and Let's Connect on LinkedIn2:33
Thank you for finishing the course: my other Udemy courses, let's connect on LinkedIn, how to share your certificate there, and where else to find me.
Let's connect on LinkedIn:
https://www.linkedin.com/in/laxmimerit/
Finished the course? Share your Udemy certificate of completion on LinkedIn and tag me, so I can give you a shout-out.
(Click the trophy above the course player, then Get Certificate, then Share or Download.)
Where to find me:
Website: https://kgptalkie.com
YouTube (English): https://www.youtube.com/@kgptalkie
YouTube (Hindi): https://www.youtube.com/@kgptalkie-hindi
GitHub: https://github.com/laxmimerit
X: https://x.com/kgptalkie
My other courses on Udemy (all on my instructor page):
https://www.udemy.com/user/laxmi-kant-tiwari/
Advanced RAG: Build & Deploy Production GenAI Apps: https://www.udemy.com/course/advanced-rag-build-deploy-production-genai-apps/
Agentic AI Projects: FastAPI, MCP, AWS Deploy & Gemini 3: https://www.udemy.com/course/langchain-ai-agent-projects/
MCP Mastery: Build AI Apps with Claude, LangChain and Ollama: https://www.udemy.com/course/mcp-mastery-build-ai-apps-with-claude-langchain-and-ollama/
LLM Fine-Tuning with Hugging Face: LoRA, QLoRA, PEFT: https://www.udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/
Jev AI and Laya Fundamentals: Decision AI for Beginners: https://www.udemy.com/course/jev-ai-and-laya-fundamentals-decision-ai-for-beginners/
SideSense Pro (Coach mode, mock interview practice before the interview):
https://sidesense.app/download

Requirements

Basic understanding of how web services work: APIs, databases, caches and load balancers
Helpful but not required: you have called an LLM API or built one small chatbot
No coding, no GPU and no math beyond simple multiplication and division

Description

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.

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.

What makes this course different

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.
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.
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.
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.
Four full mock interviews, each walked through all six steps: a customer support chatbot, a company document Q&A system, a coding assistant and an agent that takes actions.
Short lectures of about 3 to 4 minutes. Each one ends with a sample sentence you can say in the interview room.

What you will cover

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.
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.
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.
Agents and tool use: the agent loop, tool calling and MCP, context engineering, loops vs graphs, multi-agent systems, evaluating agents, guardrails on actions.
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.
Production concerns: cost control, latency budgets, reliability, security, observability, evaluation and monitoring, safe rollouts, privacy.
Self-hosting and GPU sizing: GPUs for N users, quantization, inference servers, long context, API vs your own GPUs, splitting a model across GPUs.
Mock interviews: four complete designs, the follow-up questions interviewers love and a one-page interview checklist.

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.

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.

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.

Who this course is for:

Software engineers preparing for GenAI, AI engineer or ML engineer system design interviews
Backend engineers moving into LLM products who need to explain how those products scale
Engineers who built a chatbot demo and now have to make it reliable, safe and affordable
Tech leads and architects who review LLM designs and want a shared vocabulary with their team
Not for you if you want hands-on coding: this is a theory course with no code

Mastering Gen AI System Design Interviews

What you'll learn

Explore related topics

Course content

Start here3 lectures • 4min

The Interview and the Framework6 lectures • 21min

Inside the Model: Core Building Blocks13 lectures • 47min

RAG Systems in Depth11 lectures • 40min

Agents and Tool Use7 lectures • 25min

System 1 and System 26 lectures • 22min

Production Concerns8 lectures • 29min

Self-Hosting and GPU Sizing6 lectures • 24min

Mock Interviews14 lectures • 53min

Bonus1 lecture • 3min

Requirements

Description

Who this course is for: