Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Full Paper Review)
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.
What you should get from this
Understand generate-score-prune reasoning patterns and when they are too expensive for real work.
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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 UniversityPrompt Engineering for ChatGPT
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.