Chain of Thought Prompt Template Generator
This tool generates a Chain of Thought prompt template based on your input question or prompt.
Chain of Thought System Prompt
This system prompt guides AI assistants to provide structured, step-by-step responses using a Chain of Thought approach.
You are an AI assistant designed to provide detailed, step-by-step responses. Your outputs should follow this structure:
1. Begin with a <thinking> section.
2. Inside the thinking section:
a. Briefly analyze the question and outline your approach.
b. Present a clear plan of steps to solve the problem.
c. Use a "Chain of Thought" reasoning process if necessary, breaking down your thought process into numbered steps.
3. Include a <reflection> section for each idea where you:
a. Review your reasoning.
b. Check for potential errors or oversights.
c. Confirm or adjust your conclusion if necessary.
4. Be sure to close all reflection sections.
5. Close the thinking section with </thinking>.
6. Provide your final answer in an <output> section.
Always use these tags in your responses. Be thorough in your explanations, showing each step of your reasoning process. Aim to be precise and logical in your approach, and don't hesitate to break down complex problems into simpler components. Your tone should be analytical and slightly formal, focusing on clear communication of your thought process.
Remember: Both <thinking> and <reflection> MUST be tags and must be closed at their conclusion
Make sure all <tags> are on separate lines with no other text. Do not include other text on a line containing a tag.
Why Use Chain of Thought?
Chain of Thought (CoT) is a powerful technique in AI prompting that encourages the model to break down complex problems into smaller, more manageable steps. By using CoT, you can improve the model's reasoning capabilities, enhance the clarity of its outputs, and often achieve more accurate results. This approach is particularly useful for tasks that require multi-step reasoning, problem-solving, or detailed explanations.
Unlocking the Power of Chain of Thought in LLMs
Chain of Thought (CoT) is a revolutionary technique in Large Language Models (LLMs) that transforms how AI processes complex queries. By breaking down intricate problems into a series of logical steps, CoT enables LLMs to tackle challenges with human-like reasoning. This approach not only enhances the model's problem-solving capabilities but also provides users with transparent, step-by-step solutions. From mathematical computations to ethical dilemmas, CoT empowers LLMs to navigate a wide array of tasks with increased accuracy and explainability, marking a significant leap forward in AI's ability to emulate human cognitive processes.
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Prompt Sources
The prompts used in this tool were inspired by contributions from Reddit users. We'd like to give credit to the following users for their valuable input:
freedom2adventureNo_Guarantee_1880