Tina Huang. Tina compresses Google's full prompting course into the two things a library actually stores: a five-part task/context/references/evaluate/iterate framework, and four named iteration moves to tighten a prompt over time. She also walks through agent and chain-of-thought patterns, so the same video doubles as the entry point for the more advanced templates you'll add to the library later.
Watch it here for what a library-grade entry should store — the five framework fields plus named iteration moves — rather than as another prompting course.
Turn one-off prompts into reusable patterns with evaluation notes and iteration rules.
Comfortable prompting on your own; this is about turning that into shared assets.
Last reviewed: May 18, 2026
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Hand-picked external courses that go deeper on this topic.
DeepLearning.AIIsa 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."
Coursera · Vanderbilt UniversityDr. 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.
Microsoft LearnMicrosoft 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.
