CourseLarge Language Models: The Complete Course · Module 4: Prompt Engineering · part 28 of 103
Part 28 · Module 4: Prompt Engineering

Lesson 1: What a prompt is made of, and what the model reads

22 min read·13 Oct 2026
Module 4 · Lesson 1 · About 50 minutes · Needs: Module 1, Lesson 3 (chat templates and the system prompt) and Module 2, Lesson 1 (counting tokens)

The situation

It is Monday 22 June 2026, week 4. Your triage prompt is a Python string copied into three notebooks, and the copies have drifted. Maya wants to reword one priority definition and asks which version produced last Friday's results. Nobody can say.

This week the prompt becomes a versioned file, with a parser for its replies and a clear line between your instructions and the customer's words. You follow one ticket throughout: T-1001, Ana Duarte's duplicate charge of 288 USD.

T-1001, FROM FILE TO LABELS prompt file triage_v1 render 2 messages template 445 tokens model a reply parser checks it labels or reason section 2 section 3 section 3 section 4
T-1001 travels from a prompt file through rendering and a chat template (blue: your system) to the model (amber) and back through a parser to labels (green): this lesson is each step.

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