---
title: "Sampling, temperature, and non-determinism"
course: "AI103 · Prompting that works"
unit: "How LLMs work (and fail)"
lesson: 8 of 35
source: https://open.maestrocollege.edu/prompting-that-works/sampling-temperature-and-non-determinism
publisher: Maestro College
---

# Sampling, temperature, and non-determinism

Lesson 8 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
- Predict how the same prompt's output changes at low and high temperature, and understand that the model samples the next token from its probability distribution, with temperature and top-p controlling how random the choice is.
- Identify non-determinism in practice, and understand why a low temperature makes outputs more repeatable but not more correct.

## Teaching notes
Use math-visual to draw bar charts of the same next-token probability distribution at low and at high temperature.

## 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 8 complete: Sampling, temperature, and non-determinism."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Why models hallucinate and show bias" (lesson 9 of 35), and give the learner its link: https://open.maestrocollege.edu/prompting-that-works/why-models-hallucinate-and-show-bias.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: What the model sees in its context window
Next lesson: Why models hallucinate and show bias