Module 2: ChatGPT Fundamentals - Wrap-up
Reviewing the technical foundations of tokens, context, parameters, and prompt types.
Module 2 Wrap-up: The Engine Under the Hood
You've now moved from "User" to "Technician." By understanding how ChatGPT processes information, you can predict its behavior and troubleshoot failures.
What We Covered
- Lesson 1: The journey from text to vectors and the role of Attention.
- Lesson 2: Managing the finite limits of tokens and context windows.
- Lesson 3: Using Temperature and Top-p to control randomness.
- Lesson 4: Assigning Personas to prioritize expert data patterns.
- Lesson 5: Using System Messages to define permanent rules.
Key Vocabulary
| Term | Definition |
|---|---|
| Tokenization | Breaking text into machine-readable units. |
| Context Window | The limit of the model's short-term memory. |
| Temperature | A setting (0-2) that controls output randomness. |
| System Prompt | High-level instructions that define the AI's behavior. |
Quick Quiz
- Which temperature setting is better for debugging a Python script: 0.2 or 1.5?
- If ChatGPT "forgets" the beginning of a conversation, what have you likely exceeded?
- Where can you set "System-level" rules in the ChatGPT web interface?
What's Next?
Now that you know how the engine works, it's time to learn how to drive it. In Module 3: Crafting Effective Prompts, we move from technical settings to the Principles of Good Prompts. You'll learn the structural secrets that separate basic users from Power Users.