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title: Tree of Thought Prompting: Teaching LLMs to Think Slowly
description: The post discusses the concept of 'Tree-of-Thought' (ToT) prompting as a method to enhance the reasoning capabilities of large language models (LLMs) like...
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# Tree of Thought Prompting: Teaching LLMs to Think Slowly

**[Towards Data Science](https://daily.dev/sources/tds)** · 8 min read · 0 upvotes · 0 comments

## Summary

The post discusses the concept of 'Tree-of-Thought' (ToT) prompting as a method to enhance the reasoning capabilities of large language models (LLMs) like GPT-4. By employing ToT, these models can explore multiple reasoning paths akin to a decision tree, allowing for improved decision-making. The technique is assessed through a case study where a ToT-powered LLM agent plays Minesweeper, demonstrating its ability to simulate logical, step-by-step thoughts and yielding high accuracy rates.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/tree-of-thought-prompting-teaching-llm-to-think-slowly/>

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Tags: [#ai](https://daily.dev/tags/ai), [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#gpt](https://daily.dev/tags/gpt)

[View this post on daily.dev](https://daily.dev/posts/tree-of-thought-prompting-teaching-llms-to-think-slowly-je9rs1bsa)

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