---
title: "The LLM inference lifecycle"
course: "AI103 · Prompting that works"
unit: "How LLMs work (and fail)"
lesson: 4 of 35
source: https://open.maestrocollege.edu/prompting-that-works/the-llm-inference-lifecycle
publisher: Maestro College
---

# The LLM inference lifecycle

Lesson 4 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
- Trace the lifecycle of an LLM from pretraining on large text data to a trained model with fixed parameters, then through inference on each request: the prompt becomes tokens, tokens become embeddings, the transformer produces next-token probabilities, one token is chosen and appended, and the loop repeats until the response ends.
- Distinguish what happens once during training from what happens on every request during inference, and identify which steps use the tokens and embeddings learned about so far.

## Teaching notes
Create an illustration of the LLM inference lifecycle: pretraining on text data producing a trained model with fixed parameters, then for each request a prompt flowing through tokens, embeddings, and transformer layers to next-token probabilities, with the chosen token appended to the text in a repeating loop. Reuse this same illustration in the tokens lesson.

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