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Module 3 of 1614 min read5 sub-lessons Docs view

Module 3: Tokens, Prompts, Context Windows, and AI Conversations

Master tokens, context windows, prompts, system instructions, and how a multi-turn AI conversation actually works under the hood. With cost math and worked examples.

Module 3: Tokens, Prompts, Context Windows, and AI Conversations

What this module gives you

Become fluent in the four units every AI engineer thinks in daily: tokens, prompts, context windows, and conversation turns. By the end you can predict cost, debug truncation, and design multi-turn flows that do not blow up.

Skills you will pick up

  • Counting tokens for any input or output
  • Distinguishing system, user, and assistant messages
  • Designing prompts that fit comfortably in a context window
  • Managing multi-turn conversation memory
  • Estimating API cost before you spend it

Why it matters in production

In production, the difference between a feature that scales and one that bankrupts you is almost always token discipline. Engineers who track tokens shipped reliable apps. Those who do not got bill-shocked.

Lessons in this module

  1. 1

    Lesson 3.1

    Tokens, in depth: how to count and why it matters

    3 min
  2. 2

    Lesson 3.2

    The anatomy of a prompt: system, user, assistant

    3 min
  3. 3

    Lesson 3.3

    Context windows: what fits, what gets cut, and the lost-in-the-middle effect

    3 min
  4. 4

    Lesson 3.4

    Multi-turn conversations: how memory really works

    3 min
  5. 5

    Lesson 3.5

    Token math and cost estimation

    3 min

Recap

You can count tokens, design message arrays, plan within a context window, manage multi-turn memory, and estimate cost. You are now equipped to talk like an AI engineer in any planning meeting.

Ready to start?

Open Lesson 3.1: Tokens, in depth: how to count and why it matters

Start first lesson