Yannic Kilcher. Yannic walks through the original Tree-of-Thoughts paper from Princeton/DeepMind — the generate-score-prune-backtrack loop, why it lifts Game of 24 accuracy from 4% to 74%, and where the technique stops being worth its 10× token cost. Useful precisely because it forces you to see ToT as a search algorithm, not a magic prompt prefix.
Included to make ToT concrete as a search algorithm with a real token bill — the 10x-cost boundary is the practical takeaway, not the benchmark numbers.
Understand generate-score-prune reasoning patterns and when they are too expensive for real work.
Comfortable with technical paper walkthroughs; you don't need to have read the paper.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
28 minutesUnderstand one historical account of o1, then compare a direct task specification with other supported prompt variants on your current model and workload.
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26 minutesBuild a first n8n AI-agent workflow while recognizing where tool access, memory and guardrails belong.
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."
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.
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.
