Module 2: How ChatGPT and Transformers Work (Beginner Friendly)

A clear, beginner-friendly explanation of how ChatGPT and the Transformer architecture work, from tokens to attention to next-token prediction. No heavy math required.

Module 2: How ChatGPT and Transformers Work (Beginner Friendly)

What this module gives you

Lift the hood. By the end of this module you can explain on a napkin how ChatGPT goes from your prompt to its answer, what a Transformer actually does, and why "attention" matters. No heavy math.

Skills you will pick up

Explaining tokens, embeddings, attention, and next-token prediction in plain English
Tracing the path of a single prompt through an LLM
Reasoning about why LLMs hallucinate and forget
Reading a model spec without panic

Why it matters in production

Engineers who understand the machine ship better products. They write prompts that respect how the model thinks, debug failures by tracing them to the architecture, and pick models with clear-eyed reasoning instead of brand loyalty.

Lessons in this module

Recap

You can now narrate, from memory, the path of a prompt through ChatGPT: tokenize, embed, attend, layer, predict, sample, loop. You know why hallucinations happen, why temperature exists, and how a model goes from pretraining to your screen.

Ready to start?

Open Lesson 2.1: From prompt to answer: the 6-step journey

Start first lesson