---
title: "How attention works inside transformers"
course: "AI103 · Prompting that works"
unit: "How LLMs work (and fail)"
lesson: 5 of 35
source: https://open.maestrocollege.edu/prompting-that-works/how-attention-works-inside-transformers
publisher: Maestro College
---

# How attention works inside transformers

Lesson 5 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
- Identify a transformer as a model architecture and describe, at a basic level, how attention lets it weigh and relate tokens in context when building each token's meaning.
- Trace in plain language how a prompt flows through an LLM: text to tokens, tokens to embeddings, attention layers, and finally a probability distribution over the next token.
- Identify why the placement of key instructions and facts in a long prompt affects how strongly the model attends to them.

## Teaching notes
Create an illustration that shows tokens flowing through embeddings and attention layers into a bar chart of next-token probabilities.

## 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 5 complete: How attention works inside transformers."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "How base models become assistants" (lesson 6 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/how-base-models-become-assistants.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: The LLM inference lifecycle
Next lesson: How base models become assistants