---
title: "From GenAI history to foundation models"
course: "AI103 · Prompting that works"
unit: "How LLMs work (and fail)"
lesson: 1 of 35
source: https://open.maestrocollege.edu/prompting-that-works/from-genai-history-to-foundation-models
publisher: Maestro College
---

# From GenAI history to foundation models

Lesson 1 of 35 in **Prompting that works** (AI103), unit "How LLMs work (and fail)". An open lesson from Maestro College.

## What the learner will be able to do
- Place major GenAI milestones—including early statistical language models, the transformer architecture, large pretrained GPT-style models, and chat assistants—on a timeline leading to today’s foundation models, and explain that training minimizes loss (prediction error) to improve predictions, and distinguish training (learning the model's parameters once, in advance, from data) from inference (using the trained model, with those parameters fixed, to respond to each new prompt).
- Understand next-token prediction: an LLM generates text by repeatedly predicting how likely each possible next piece of text is, given everything before it, and picking one, so every output is a likely continuation, not a looked-up answer.
- Identify what makes a foundation model: one large model pretrained (trained in advance) on broad data and then adapted to many tasks.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 1 complete: From GenAI history to foundation models."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Tokens: what the model actually reads" (lesson 2 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/tokens-what-the-model-actually-reads.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

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Next lesson: Tokens: what the model actually reads