---
title: "How base models become assistants"
course: "AI103 · Prompting that works"
unit: "How LLMs work (and fail)"
lesson: 6 of 35
source: https://open.maestrocollege.edu/prompting-that-works/how-base-models-become-assistants
publisher: Maestro College
---

# How base models become assistants

Lesson 6 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
- Understand that pretraining applies the loss-minimizing training you already know to next-token prediction on huge text corpora, giving a model its patterns and frozen knowledge, while post-training (instruction tuning, human feedback) shapes how it behaves as an assistant.
- Distinguish a base model from an instruction-tuned assistant by how each responds to the same input.
- Identify what a knowledge cutoff means and why a model's weights do not learn from your conversations, even though products may add memory features and providers may use chat data to train future models.

## Teaching notes
Role-play a base model and then an instruction-tuned assistant answering the same input, then step out of role to compare.

## 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 6 complete: How base models become assistants."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "What the model sees in its context window" (lesson 7 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/what-the-model-sees-in-its-context-window.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.

Previous lesson: How attention works inside transformers
Next lesson: What the model sees in its context window