Grokking Generative AI Fundamentals
Learn how generative AI and large language models work and how to build reliable features with them. Tokens, embeddings, RAG, tool calling, and agents explained for software engineers.
Course Overview
Generative AI is now part of everyday engineering work. Engineers call LLM APIs, review code an AI assistant wrote, and answer AI questions in interviews. Yet many use these models without understanding how they work or why they fail. This course explains how generative AI works, in plain language and with clear diagrams. You will see what happens between your request and the model's answer: how text becomes tokens and embeddings, how attention and the transformer work, and how the model picks each next word. Worked examples replace heavy math. You will then build with these models. You will call LLM APIs, write prompts that give steady results, and get structured output that your code can use. You will connect a model to your own documents with retrieval-augmented generation (RAG), and you will learn how agents use tools and when an agent is the wrong choice. The course also covers what it takes to run these features in production. You will learn how to catch hallucinations, defend against prompt injection, protect user data, and control cost and latency. The course ends with a capstone, in which you design a complete AI support assistant and then compare your design with a model solution. Along the way, you can run small Python programs inside the lessons to see each idea at work. Each teaching chapter ends with an assessment that checks your understanding, and a glossary at the end defines every key term and links it to the lesson that teaches it. The course is written for software engineers who can code but have not studied machine learning.
What you'll learn in Grokking Generative AI Fundamentals
- How AI, machine learning, deep learning, and generative AI relate, and where large language models fit.
- How an LLM turns text into tokens and embeddings, and how attention and the transformer use them.
- How diffusion models create images from noise, and how multimodal and code models extend the same ideas.
- How to write prompts that give steady results, and how to test them with evals before you ship.
- What an AI agent is, how tools and MCP work, and when an agent is the wrong choice.
- How to estimate and control the cost and latency of a GenAI feature before it launches.
- A capstone in which you design a complete AI support assistant and compare it with a model solution.
- How a model learns from data, and why training costs millions while one request costs a fraction of a cent.
- How a model picks each next word, and why the same request can return a different answer.
- How to call LLM APIs with streaming and retries, and get structured output that your code can trust.
- How to ground a model in your own documents with RAG, and when prompting or fine-tuning fits better.
- How to catch hallucinations, defend against prompt injection, and keep user data out of your logs.
- Small Python programs that run inside the lessons, with nothing to install and no API key.
Course Content
Just Enough Machine Learning
Glossary
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About the Author
Arslan Ahmad
Industry Expertise & Leadership
Arslan Ahmad is the lead author of Grokking Generative AI Fundamentals. As the founder of Design Gurus and a former FAANG hiring manager, he has worked at industry giants like Facebook (now Meta) and Microsoft.
He has conducted hundreds of system design interviews, giving him unique insight into what top tech companies look for in candidates.
The course also incorporates expertise from senior engineers at Google, Meta, Amazon, Microsoft, and Uber, ensuring you learn system design best practices from professionals who have built and scaled real-world systems.
500+
Interviews Conducted
10k+
Students Taught
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FAQs
What is Grokking Generative AI Fundamentals?
This course explains how generative AI and large language models work, then shows how to build reliable features with them, from the first API call to a production-ready design.
Who is this course for?
It is for software engineers who can code but have not studied machine learning. It also suits engineers who already call LLM APIs and want to understand why the models behave the way they do.
Do I need a background in machine learning or math?
No. Each idea is explained in plain language, with diagrams and worked examples in place of heavy math. Basic programming is enough, and the code examples are in Python.
Do I need an API key or a paid AI account?
No. The programs in the lessons run in your browser using Python's standard library and stand-in models, so they need no API key or installs. Where a lesson shows a call to a real provider, it also shows how to make that one-line swap on your own machine.
Which models and providers does the course cover?
The ideas apply to any provider, whether the model is hosted or open-weight. When a real call is needed, the code uses the OpenAI Python library, and the lessons note where field names differ between providers. One lesson covers small, local, and open models that you can run yourself.
Does the course cover RAG and AI agents?
Yes. You learn context engineering, retrieval-augmented generation (RAG), and when to choose prompting, RAG, or fine-tuning, and you build a small question-answering bot over a set of documents. You also learn what an agent is, how tools and MCP work, and how to use AI safely in your own development work.
Does it cover running GenAI features in production?
Yes. The course covers evals, cost, latency, and rate limits, and then hallucinations, prompt injection, data leakage, jailbreaks, and responsible use. It ends with a reference architecture that shows where each safeguard sits in a real feature.
Is there any practice, or is it only reading?
There is practice throughout. Many lessons include a small program you can run and change; each teaching chapter ends with an assessment that explains every option after you answer; a final assessment covers the whole course; and a capstone asks you to design a complete AI support assistant.
Will this course help with interviews?
Yes. One lesson covers how generative AI comes up in interviews and in your career, and the capstone follows the shape of a system design question. For deeper preparation, Grokking the AI System Design Interview is the natural next step.
How is this different from Grokking the AI System Design Interview?
This course teaches how generative AI works and how to build one reliable feature with it. Grokking the AI System Design Interview focuses on designing large AI systems under interview conditions. Take this course first if the basics of LLMs, RAG, and agents are still new to you.
How is this different from Grokking Modern AI Fundamentals?
Grokking Modern AI Fundamentals is a broad tour of the AI field, including search algorithms, reinforcement learning, and the social and regulatory side of AI. This course focuses on generative AI and large language models, and on how engineers build working features with them.
Is there a glossary of terms?
Yes. The last chapter is a glossary that defines every key term in the course and links each one to the lessons where it is taught, so you can look up a term without searching.
