How to Use Tree of Thoughts Prompting for Enhanced AI Results

How to Use Tree of Thoughts Prompting for Enhanced AI Results | Artificial Intelligence and Machine Learning | Emeritus

The State of AI in 2023 Report by McKinsey reveals that despite generative Artificial Intelligence (AI) being in its nascent stage, 60% of organizations are regularly using it to improve their business processes. Top AI performers are using generative AI advancements to reduce business costs, generate new revenue sources, and offer more business value by deriving AI insights. This is because of generative AI’s ability to perform complex reasoning. However, the key to using generative AI to get desired results is effective prompting. The Chain of Thoughts prompting strategy emerged as an advanced prompting technique in 2022. In 2023, researchers developed another technique called Tree of Thoughts prompting for enhancing AI results. We take a closer look at the latter to understand how to best utilize it.

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How Does Tree of Thoughts Prompting Work in Generative AI?

This prompting technique is based on the concept followed in the Chain of Thoughts prompting technique, where the user provides a multistep problem. Generative AI offers step-by-step reasoning for the output and provides the correct solution. 


In Tree of Thoughts prompting, the Language Learning Model (LLM) breaks down a query or problem into several intermediate steps. This is called decomposing the query into a series of thoughts. The LLM explores multiple solution paths parallelly through backtracking. Thus, it creates a series of thoughts rather than a single chain as created in the Chain of Thoughts technique. The several intermediate steps in this technique enable LLMs to plan the solution by finding the correct reasoning, thus enhancing AI results.

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What are the Benefits of Using Tree of Thoughts Prompting in AI Models? 

The following are the key benefits of using Tree of Thoughts prompting:

1. Enhanced Problem-Solving

Tree of Thoughts prompting is an advanced version of Chain of Thoughts prompting. It has the ability to explore multiple solutions and analyze each solution path to find the most favorable outcome.

2. Better User Experience

This technique offers a structured roadmap for AI models, enabling them to offer more researched and comprehensive solutions. Thus, it improves the overall user experience in AI for professionals.

3. Offers Better Contextual Depth

It imitates human thinking abilities by considering various thoughts and thus offers better contextual reasoning.

4. Parallel Exploration of Topics

Tree of Thoughts prompting enables AI models to parallelly explore multiple paths. The model can backtrack the input and self-evaluate choices to find the next course of action. Therefore, instead of generating a single output, it deliberately plans and explores multiple solutions and suggests the best outcome.

How Can it Help in Improving AI Results for Professionals?

The Tree of Thoughts prompting enables deliberate planning and exploration by an AI model. It generates thoughts in the form of branches, and each thought is solved independently before moving to the next step of problem-solving. Simply put, Tree of Thoughts prompting works on the concept of trial and error for delivering the best solution. This AI model optimization technique works on the Tree of Thought (ToT) framework. It uses several components, including a prompter agent, a checker module, a memory module, and a ToT controller for more effective problem-solving.

Tree of Thought prompting offers enhanced reasoning and problem-solving because it can plan, look ahead, and backtrack the thoughts to find the best solution. Here is how Tree of Thoughts prompting works:

  • Thought decomposition or breaking down the query into multiple thoughts
  • Generating multiple options for each step
  • Evaluating potential outcomes of each step through backtracking

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Are There Any Limitations or Challenges Associated With This Technique?

Even though Tree of Thoughts prompting and other advanced prompting techniques show remarkable problem-solving capabilities, they are not considered to be highly effective in long-range reasoning tasks that require long-term planning and in-depth exploration. The following are the key challenges associated with Tree of Thoughts prompting: 

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1. Limited Research 

Research on Tree of Thoughts prompting so far has been used for simpler tasks that were difficult to solve through GPT-4. It is, therefore, likely that Tree of Thoughts prompting may not be as efficient while making real-world decisions.

2. Complexity of Tasks

This technique may not be effective in highly technical fields such as coding or robotics. This is because of the higher complexity of tasks in such fields. Therefore, Tree of Thoughts prompting in AI for professionals requires more research and efforts by the open-source community for AI model optimization.

How Can Managers and Employers Leverage This Technique to Enhance AI-Based Decision-Making?

In problem-solving, there is no defined method or a single solution. Professionals explore multiple options and use permutations and combinations to find the optimum solution. Tree of Thoughts prompting is based on the same concept. The branches represent various solution paths similar to organized decision-making. Therefore, managers and employers can leverage the Tree of Thoughts prompting to make strategic decisions. It can assist businesses in complicated decision-making that requires a lot of planning and thorough inputs. Managers and employers can use Tree of Thoughts prompting in the following ways:

  • Gathering real-time insights and offering personalized solutions to customers
  • Boosting operational efficiency by tackling business challenges easily
  • Increasing sales by offering customized solutions to customers

ALSO READ: Prompt Engineer Jobs: Top 3 Reasons They are on the Rise

According to a survey by KPMG, 71% of respondents believe they will implement a generative AI solution by 2025. However, one of the most significant barriers to effective AI implementation is the lack of skilled talent. Organizations need skilled resources to generate effective prompts for leveraging the maximum potential of generative AI advancements. Therefore, prompt engineering is emerging as a promising career due to the tremendous growth of AI. Emeritus’ online artificial intelligence and machine learning courses help professionals learn the required skills and practical experience in AI. The courses also offer insights into the latest AI trends, offering a competitive edge. Explore Emeritus’ online artificial intelligence courses and machine learning courses to accelerate your career!

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Content Writer, Emeritus Blog
Sneha is a content marketing professional with over four years of experience in helping brands achieve their marketing goals. She crafts research-based, engaging content, making sure to showcase a bit of her creative side in every piece she writes. Sneha spends most of her time writing, reading, or drinking coffee. You will often find her practicing headstands or inversions to clear her mind.
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