Tree of thoughts
Also called ToT, tree-of-thought prompting
A prompting approach where the model explores several possible paths, judges them, and continues with the most promising.
Tree of thoughts extends chain-of-thought prompting from a single line of reasoning to a branching search. At each step, the model generates several possible next steps, evaluates how promising each is, and continues with the best ones, backing up when a path hits a dead end. The method was introduced in a 2023 research paper and helped on puzzles and planning tasks where the first idea is often wrong.
In its full form, tree of thoughts is implemented in code that manages the branches and calls the model many times. But the core idea is easy to use in an ordinary prompt: ask for several distinct approaches, have the model assess the strengths and risks of each, then develop the strongest one, or combine the best parts.
This is useful for decisions, debugging, strategy and creative work, anywhere committing early to one idea is risky. Reasoning models now do some of this exploration internally, but explicitly asking for alternatives still helps you see options you might otherwise miss.
Example
"Give me three different ways to reduce customer churn, each from a different angle (product, pricing, support). Rate each on cost, speed and likely impact. Then develop the best one into a 30-day plan." The model explores before it commits.
Common mistake
Asking for alternatives but getting three versions of the same idea. Force real variety by naming different angles, constraints or assumptions for each branch, and ask the model to explain how the options differ before evaluating them.