36 minutesEvery ChatGPT Feature In 37 Minutes
Configure personalization deliberately instead of letting memory, instructions and project context blur together.
Anthropic. Examples are one of the ten ingredients Anthropic walks through here, and you can watch them get added to a real customer prompt and change the model's output. Useful if you want to see few-shot used inside a fuller prompt rather than as an isolated trick.
Included so you can see few-shot working inside a full prompt rather than as an isolated trick; watch for how the output changes when the examples land.
You will be able to place examples inside a fuller prompt structure and anticipate how they change what the model produces.
Know the basic prompt structure from the article first — examples are one layer among several here.
Last reviewed: May 18, 2026
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Hand-picked external courses that go deeper on this topic.
Dr. Jules White
The academic complement to DeepLearning.AI's short course — same discipline, longer arc, written for people who don't code. Dr. White teaches prompting as a set of reusable patterns (Ask for Input, Outline Expansion, Fact Check List, Menu Actions) rather than tricks. After this you'll prompt LLMs like a designer, not a guesser.
Isa Fulford · Andrew Ng
Ninety minutes to a year's worth of intuition. If you've started writing code that calls an LLM — or you're about to — this is the most efficient course online for closing the gap between "playing with ChatGPT" and "shipping a feature that calls an LLM."
Microsoft Learn
Prompt engineering, but for the tool most office workers will actually touch first. Microsoft's own four-part prompting framework (goal, context, source, expectation) is a genuinely useful mental model, and unlike the generic ChatGPT prompting courses already in our catalog, this one is grounded entirely in Microsoft 365 Copilot's specific quirks and grounding behavior.