What happens inside ChatGPT, told through 100 years of history. What exactly is a large language model (LLM) like ChatGPT doing inside? This course aims to let you explain the basic principles in your own words, even if you have no background in programming or math. The approach is a little unusual. Instead of jumping straight to the latest models, we walk through roughly 100 years of language model history in order, from Markov chains in 1913 to the hybrid architectures of 2026. Every technique was born to solve a problem the previous technique had. When you follow each problem together with its solution, even intimidating cutting-edge techniques fall into place naturally.
Figures, analogies, and a minimum of math. Throughout the course we use a single analogy: “how a reader remembers a book.” Looking only at the last few words, summarizing into a single memo, looking back at sticky notes, running a full-text search every time, and returning to a smart summary memo. We understand each model as a different way of remembering.
Math is kept to a minimum, and whenever it is truly necessary we always explain in the order figure, analogy, then equation. The course is built so that the story still holds together if you skip the equations. The only arithmetic you need is addition, multiplication, and a feel for proportions. For Self-Attention, we trace a single hand calculation with small numbers so the mechanism sinks in. Each section ends with a 3-line summary and a quiz, and once you can answer it you are ready to move on.
