---
title: "What the model sees in its context window"
course: "AI103 · Prompting that works"
unit: "How LLMs work (and fail)"
lesson: 7 of 35
source: https://open.maestrocollege.edu/prompting-that-works/what-the-model-sees-in-its-context-window
publisher: Maestro College
---

# What the model sees in its context window

Lesson 7 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 on each call the model sees only the tokens in its context window (system instructions, conversation history, retrieved documents, tool results) and keeps nothing between calls unless code sends it again.
- Understand inference as a generation loop in which each new token is added to the context before the next is predicted, so longer outputs take more time and tokens.
- Identify what happens when a conversation grows past the context window, and how code handles it by trimming or summarizing older turns.

## 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 7 complete: What the model sees in its context window."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Sampling, temperature, and non-determinism" (lesson 8 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/sampling-temperature-and-non-determinism.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 base models become assistants
Next lesson: Sampling, temperature, and non-determinism