INTRODUCTION
Have you ever wished your AI could think more like a human, breaking down complex problems step-by-step or brainstorming solutions like a team of experts? Well, you’re in luck. Today, I’ll show you how AI can achieve this through two powerful techniques: Chain of Thought and Tree of Thought prompting. Think of these techniques as sisters—similar in their purpose but different in their approach. In this article, we’ll dive into both methods, show you how to use them with examples, and highlight how they differ from each other. Read More
UNDERSTANDING CHAIN-OF-THOUGHT PROMPTING
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Let’s start with the Chain-of-Thought technique. This may sound complicated at first, but it’s simply a prompt-based learning approach that involves constructing a series of prompts or questions to guide the AI in generating a desired response.
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In short, you write a prompt that includes a question and an accurate answer to provide the necessary context and step-by-step reasoning for the model. Then, you pose a different question to be answered using the same line of reasoning.
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Example:
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Prompt with answer:
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Q: A small tech startup has a budget of $1000 for software tools and needs to maximize the number of different tools they can subscribe to. The costs of the tools are as follows:
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Project Management Tool: $200 per year
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Communication Tool: $150 per year
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Design Tool: $100 per year
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Analytics Tool: $250 per year
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Given the budget, how should they allocate their spending to subscribe to the maximum number of tools?
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A: To maximize the number of different tools within a $1000 budget, the startup should consider the cost of each tool. Here’s a possible allocation:
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– Design Tool: $100
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– Communication Tool: $150
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– Project Management Tool: $200
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– Analytics Tool: $250
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Total: $100 + $150 + $200 + $250 = $700
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This allocation totals $700, leaving $300 in the budget, which can be used for future needs or to upgrade one of the tools.
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New Prompt:
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Q: A marketing department has a budget of $500 to spend on online advertising and wants to reach the maximum number of impressions. The costs per impression for different platforms are as follows:
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Social Media Ads: $0.50 per impression
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Search Engine Ads: $0.75 per impression
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Display Network Ads: $0.40 per impression
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Given the budget, how should they allocate their spending to achieve the maximum number of impressions?
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EXPLORING TREE-OF-THOUGHT PROMPTING
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Now, let’s move on to the Tree-of-Thought method. As I mentioned earlier, these techniques are like sisters—both aim to solve complex problems but in different ways.
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The Tree-of-Thought approach involves hierarchically structuring a prompt or query, similar to a tree, to specify the desired line of thinking or reasoning for the model.
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Example:
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- Collaborative Expert Simulation:
Identify and behave as three different experts who are appropriate for answering this question. All experts will write down their step and reasoning, then share it with the group. After that, all experts will proceed to the next step. At each step, all experts will score their peers’ responses between 1 and 5, with 1 being highly unlikely and 5 being highly likely. If any expert is judged to be wrong at any point, they leave. After all experts have provided their analysis, you then analyze all three analyses and provide either the consensus solution or your best guess solution. The question is…
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- Collaborative Expert Simulation:
Simulate three brilliant, logical experts collaboratively answering a question. Each one explains their thought process in real-time, considering the prior explanations of others and openly acknowledging mistakes. At each step, whenever possible, each expert refines and builds upon the thoughts of others, acknowledging their contributions. They continue until there is a definitive answer to the question. For clarity, your entire response should be in a markdown table. The question is…
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- Sequential Expert Input:
Imagine three different experts are answering this question. All experts will write down one step of their thinking, then share it with the group. Then all experts will proceed to the next step. If any expert realizes they’re wrong at any point, they leave. The question is…
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CONCLUSION
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So, to sum it up:
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Chain of Thought is like taking the AI by the hand and walking it through a problem step-by-step. It helps the AI think in a clear, logical way.
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Tree of Thought, on the other hand, is like having a group of experts working together, each adding their own ideas and checking each other’s work to solve a problem.
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Both techniques are great for helping AI solve tough problems, but they do it in different ways. Try using these methods the next time you work with AI and see how much better the results can be!
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Stay tuned for our next article, “Mirrors of Thought: The Power of Analogical Prompting,” where we’ll explore even more cool ways to get the best out of AI!
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Marika Górska