AI Fundamentals / Overview
AI & LLMs · LLMs explained
AI Fundamentals
Understand how large language models work under the hood — from tokenization and attention to prompt engineering and RAG. No math prerequisites; every concept is anchored with interactive exercises.
Course progress0% · 0/14 lessons
Modules
Module 1 · Foundation
How LLMs think
What large language models actually do — prediction, tokenization, and how meaning gets encoded as vectors.
3 lessons
Module 2 · Architecture
The Transformer
Inside the engine — self-attention, multi-head attention, and the layer stack that makes LLMs possible.
3 lessons
Module 3 · Architecture
Controlling output
Temperature, sampling strategies, and prompt engineering — how to steer the model toward the response you want.
3 lessons
Module 4 · Extension
Advanced techniques
RAG, fine-tuning, and understanding hallucination — when prompting isn't enough and how to ground model outputs.
3 lessons
Module 5 · Application
AI in practice
Choosing the right model, understanding cost-performance tradeoffs, and evaluating model quality with benchmarks.
2 lessons