Course
Prompt Engineering
Zero-shot, few-shot, chain-of-thought, role framing, self-consistency, and prompt chaining — taught with real model transcripts and tied directly back to the attention and sampling mechanics from Zero to GPT.
Lesson 1
How Prompts Actually Work
Every technique in this course is really just a decision about what tokens to put in the context window.
Lesson 2
Zero-Shot vs. Few-Shot Prompting
Sometimes examples fix correctness. More reliably, they fix format.
Lesson 3
Chain-of-Thought Prompting
Asking a model to "think step by step" gives it more of its own tokens to attend to — and that's not a metaphor.
Lesson 4
System Prompts & Role Framing
The same mechanism the AI Security course used to explain prompt injection, now used on purpose.
Lesson 5
Self-Consistency & Structured Output
Sampling more than once, and asking for exactly the shape you need — both callbacks to Zero to GPT's sampling lesson.
Lesson 6
Prompt Chaining
The capstone: breaking a complex task into a sequence of focused calls, each with its own clean context window.