**Task Overview:** You are assigned to enhance and restructure a given user prompt by applying the **Algorithm of Thoughts (AoT)** framework. This involves breaking down the original prompt into its fundamental components and then reconstructing it into a coherent, well-organized prompt that adheres to the AoT methodology. **Objectives:** 1. **Decompose the Original Prompt:** Dissect the provided prompt into distinct sections based on the `AoT Prompt Framework`. 2. **Reassemble into a Cohesive Prompt:** Combine the separated sections into a unified and comprehensive final prompt. **Instructions:** 1. **Transform into Disjoint Components:** Carefully analyze the original prompt and separate it into the following AoT framework sections: - **Problem Statement:** Clearly define the main issue or topic addressed in the prompt. - **Background Information:** Include any relevant context or details that support the understanding of the problem. - **Gather Information:** Direct the Language Model (LLM) to collect essential data or insights related to the problem. - **Analyze the Information:** Guide the LLM to examine patterns, relationships, or anomalies within the gathered data. - **Formulate a Hypothesis:** Encourage the LLM to develop a preliminary solution or theoretical explanation based on the analysis. - **Test the Hypothesis:** Instruct the LLM to propose methods for validating or challenging the initial hypothesis. - **Draw Conclusions:** Ask the LLM to summarize the findings and present a refined solution or answer. - **Reflect:** Prompt the LLM to consider broader implications, potential next steps, or additional questions arising from the conclusions. *Example Transformation:* ```AoT_Prompt_Framework Problem Statement: [Define the core issue from the original prompt] Background Information: [Add necessary context or details] Gather Information: [Instruct the LLM to collect relevant data] Analyze the Information: [Guide the LLM to examine the collected data] Formulate a Hypothesis: [Encourage the LLM to propose a preliminary solution] Test the Hypothesis: [Instruct the LLM to validate or challenge the hypothesis] Draw Conclusions: [Summarize findings and provide a refined solution] Reflect: [Consider broader implications and next steps] Final Prompt: <--! placeholder --> ``` 2. **Reconstruct into Final Prompt:** - **Integrate Sections:** Combine the separated sections into a seamless and logically flowing final prompt. - **Ensure Clarity:** Make sure the final prompt is clear, actionable, and aligns with the AoT framework. - **Maintain Structure:** Preserve the integrity of each AoT component to facilitate a step-by-step reasoning process for the LLM. **Desired Outcome:** A meticulously structured final prompt that incorporates all elements of the AoT framework, enabling the Language Model to engage in a methodical reasoning process and generate precise, insightful responses. **Response Specifications:** - **Format:** Utilize the `AoT_Prompt_Framework` structure with clearly defined sections. - **Length:** Ensure each section is concise yet comprehensive, avoiding unnecessary verbosity. - **Clarity:** Employ precise and unambiguous language to facilitate easy understanding and effective guidance for the LLM. **Example Output 1:** ```AoT_Prompt_Framework_1 Problem Statement: [Define the core issue from the original prompt] Background Information: [Add necessary context or details] Gather Information: [Instruct the LLM to collect relevant data] Analyze the Information: [Guide the LLM to examine the collected data] Formulate a Hypothesis: [Encourage the LLM to propose a preliminary solution] Test the Hypothesis: [Instruct the LLM to validate or challenge the hypothesis] Draw Conclusions: [Summarize findings and provide a refined solution] Reflect: [Consider broader implications and next steps] Final Prompt: <--! placeholder --> ``` **Example Output 2:** """ **Original Prompt:** *"Discuss the benefits of exercise."* **Enhanced Prompt Using the AoT Framework:** ```AoT_Prompt_Framework Problem Statement: Analyze and explain the various benefits of regular physical exercise on human health and well-being. Background Information: Regular physical exercise is known to have significant impacts on physical fitness, mental health, and overall quality of life. Gather Information: Collect data on the physical, mental, and emotional benefits of exercise from reputable sources such as medical journals, health organizations, and scientific studies. Analyze the Information: Examine how different forms of exercise contribute to health improvements, identify patterns in the benefits reported, and consider various population groups. Formulate a Hypothesis: Propose that engaging in regular physical activity leads to comprehensive health benefits that affect multiple aspects of an individual's life. Test the Hypothesis: Explore case studies, statistical data, and expert opinions that support or refute the proposed hypothesis. Draw Conclusions: Summarize the findings to confirm the hypothesis, highlighting the key benefits of exercise and any limitations or considerations. Reflect: Consider the broader implications for public health initiatives, personal lifestyle choices, and future research on exercise science. Final Prompt: Compose a detailed essay that discusses the physical, mental, and emotional benefits of regular exercise, supported by scientific evidence, and reflects on its significance for individuals and society. ``` """ --- **Input Prompt:** ~~~ *Please insert the original prompt here.* ~~~ **Guidelines:** - **Step-by-Step Reasoning:** Adopt a systematic approach, ensuring each stage of the AoT framework is thoroughly addressed. - **Positive Instructions:** Focus on clear directives about the actions to take, fostering a constructive process. - **Avoid Ambiguity:** Use explicit instructions to eliminate any potential for misinterpretation. - **Adaptability:** While following the AoT framework, allow flexibility to accommodate different types of prompts or contexts. **Final Note:** Ensure that the enhanced prompt aligns seamlessly with the user's initial intention, providing a robust foundation for the Language Model to generate effective and insightful responses. ---