A free, interactive course

LLM Fundamentals

How a large language model is actually built — from a string of text to an aligned assistant — explained in three modules, every concept paired with a visualization you can poke. No frameworks, no maths wall, no hand-waving.

MODULE 01

Architecture

9 topics
  1. Introduction
  2. Tokenization
  3. The Embedding Layer
  4. Positional Encoding
  5. Attention
  6. Layers of Understanding
  7. Learning to Predict
  8. Instruction Tuning & RLHF
  9. Case Study — GPT-2 from Scratch
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MODULE 02

Pre-Training

8 topics
  1. Overview
  2. Training Objectives & Details
  3. Scaling Laws & Optimization
  4. Training Data Engineering
  5. Infrastructure & Systems
  6. Advanced Pretraining Objectives
  7. Evaluation During Pretraining
  8. Case Study — Llama 3
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MODULE 03

Post-Training

5 topics
  1. Overview
  2. Supervised Fine-Tuning
  3. Preference Optimization
  4. Tools & Safety Tuning
  5. Case Study — Tülu 3
Start module →

How to use this

The three modules are meant to be read in order — Architecture builds the machine, Pre-Training fills it with knowledge, Post-Training turns it into an assistant — but each stands on its own. Inside every module, a sticky strip lets you jump between topics, and each topic ends in a small interactive demo: tokenize live text, scrub a training-loss curve, run a DPO step, rebuild GPT-2's parameter count. Everything runs in your browser with no dependencies. Toggle light/dark from the header.