Foundations Overview
What Foundations covers and what you'll be able to build by the end, before you start setting up your machine.
Chapter 0: Set Up Your Machine
Install uv, VS Code, and Ollama so every hands-on lab in this Generative AI course runs locally and for free.
Chapter 1: What Is AI, Really?
What AI, machine learning, deep learning, and generative AI actually mean, and how they relate to each other.
Chapter 2: What Is a Large Language Model?
How large language models like ChatGPT and Claude predict text one token at a time, explained with a hands-on lab.
Bonus: Using AI Responsibly
A short, no-lab chapter on using AI tools responsibly, data privacy, bias, verifying what a model tells you, and IP basics.
Chapter 3: Prompting 101
Zero-shot, few-shot, and system prompts, the biggest lever you have over an LLM's output, with a runnable comparison.
Chapter 4: What Is an Embedding?
What an embedding is and how it turns text into vectors that place similar meanings near each other.
Chapter 5: What Is a Vector Database, and Why?
What a vector database is and why it makes searching millions of embeddings for the closest match fast.
Chapter 6: What Is RAG?
What Retrieval-Augmented Generation (RAG) is and how it turns an LLM into an open-book answerer.
Chapter 7: What Is an AI Agent?
What an AI agent is and how it differs from a plain chatbot, deciding what to do next and calling tools.
Chapter 8: Capstone: A Q&A Bot Over Your Own Documents
The Foundations capstone — combine embeddings, a vector database, and RAG into a working Q&A bot over your own documents.