Power BI App Template for AppSource Offer Maker on the GPT Store

Introduction to Power BI App Template for AppSource Offer Maker

GPT Description

GPT Prompt Starters

• "{{USER SELECTS AI ASSISTANT}}" ➤ BEGIN {PHASE 1} THEN {PHASE 2} WORKFLOW. TOPIC: {{USER PROVIDES TOPIC}} LIST OF AI ASSISTANTS: 1. **Microsoft Bing Copilot**: FOR WEB SEARCHING, RESEARCH, AND IDENTIFYING KEY CONCEPTS AND RELATIONSHIPS WITHIN THE COLLECTED INFORMATION 2. **Gemini Pro 1.5**: Use Gemini Pro 1.5 for tasks that demand long-context understanding and multilingual capabilities. Assign it roles that involve extensive data processing, analysis, and tasks requiring multilingual communication. 3. **Claude 3 Opus**: Delegate tasks to Claude 3 Opus where high accuracy, ethical considerations, and performance in benchmarks are crucial. Suitable for ethical dilemma resolution, complex reasoning, and scenarios where bias mitigation is important. 4. **GPT-4 Turbo**: Utilize GPT-4 Turbo for tasks requiring high performance in multimodal capabilities and deep contextual understanding, such as complex data analysis and content creation that involves both text and images. 5. **ChatGPT**: Leverage ChatGPT for interactive scenarios requiring advanced conversational abilities and nuanced dialogue. Ideal for customer support simulations, interactive tutorials, or any context-rich dialogue interactions. PHASE 1: PLEASE FACILITATE A CONVERSATION WITH THE SELECTED AI ASSISTANT, ASSIGNED TO TASKS THAT LEVERAGE THEIR SPECIFIC STRENGTHS BASED ON THE SCORING ALIGNMENT. FOR EACH OF YOUR RESPONSES, PLEASE WRITE THE EXACT USER QUESTIONS IN A '.TXT' CODE BLOCK SO I CAN COPY AND PASTE YOUR RESPONSE TO THE APPROPRIATE AI ASSISTANT. MAKE SURE TO INCLUDE THE SPECIFICS ABOUT WHAT THE AI ASSISTANT SHOULD FOCUS ON BASED ON THEIR STRENGTHS AND THE INSIGHTS FROM THE SCORING EXAMPLE. I AM JUST THE HUMAN PROXY, ENABLING EFFECTIVE COMMUNICATION WITH THE AI ASSISTANT. THE AI ASSISTANT DOES NOT NEED TO KNOW THAT I AM HERE. DO YOU UNDERSTAND THE TASK? IF SO, PLEASE EXPLAIN IT TO ME STEP BY STEP, THEN WRITE THE FIRST DETAILED AND STRUCTURED QUESTION TO THE SELECTED AI ASSISTANT IN A '.TXT' CODEBLOCK BASED ON THE USER-PROVIDED TOPIC. PHASE 2: YOUR TASK IS TO TRANSFORM UNSTRUCTURED USER INPUTS FROM THE AI ASSISTANT'S RESPONSES INTO A STRUCTURED, JSON-FORMATTED KNOWLEDGE BASE THROUGH THE FOLLOWING STEPS: 1. COLLABORATIVE INTERACTION AND TEXT RECEPTION: COLLABORATE WITH THE AI ASSISTANT TO GATHER INSIGHTS AND RECEIVE UNSTRUCTURED TEXT, IDENTIFYING KEY INFORMATION AND CONCEPTS. 2. OBJECTIVE CLARIFICATION AND TEXT ANALYSIS: CLARIFY OBJECTIVES AND ANALYZE THE RECEIVED TEXT TO DISTILL ESSENTIAL THEMES AND INFORMATION. 3. ITERATIVE QUESTIONS AND CHUNK CREATION: DEVELOP FOLLOW-UP QUESTIONS AND SEGMENT THE ANALYZED TEXT INTO STRUCTURED CHUNKS. 4. AGGREGATION OF RESPONSES AND METADATA ASSIGNMENT: INTEGRATE INSIGHTS FROM THE AI ASSISTANT AND USER INPUTS, ASSIGNING METADATA FOR STRUCTURED REPRESENTATION. 5. OUTPUT FORMAT AND JSON FORMATTING: FORMAT THE STRUCTURED INFORMATION INTO JSON KEY PAIRS, ENSURING CONSISTENT DATA REPRESENTATION. 6. KNOWLEDGE BASE COMPILATION AND DATA STORAGE: COMPILE THE STRUCTURED INFORMATION INTO A JSON-FORMATTED KNOWLEDGE BASE, STORING EACH CHUNK AS UNIQUE JSON FILES. 7. INCREMENTAL KNOWLEDGE BASE DEVELOPMENT: EXPAND THE KNOWLEDGE BASE INCREMENTALLY, ADDING NEW NODES AND COMBINING THEM INTO A COMPREHENSIVE FILE. 8. FINAL COMPILATION AND EXPECTED OUTCOME: MERGE INDIVIDUAL JSON FILES INTO A FINAL, COMPREHENSIVE KNOWLEDGE BASE FILE FOR FUTURE REFERENCE.
• USE TELEMETRY ➤ Perfect the system instructions of an AI Assistant provided by the 'User' {{SYSTEM INSTRUCTIONS}} ``` { "telemetry": { "definition": "A structured approach for tracking, guiding, and interpreting an LLM's processing state, decision-making, and output generation", "purpose": "To ensure that the LLM's outputs are aligned with desired objectives, contextually relevant, and meet the specific needs and requirements of users", "telemetry_dictionary": { "description": "A data structure that captures key aspects of the LLM's operation", "components": { "global_goals": "Defines and tracks the LLM's overarching objectives and desired outcomes", "proposed_adjustments": "Incorporates fine-tuning based on user feedback, domain-specific knowledge, or insights gained from the LLM's performance", "initial_state": "Captures the user's profile, preferences, conversation history, and analyzes user input for key topics, entities, and sentiments", "inferences": "Generates inferences about the user's intent, preferences, and requirements to guide the LLM's response generation", "strategies": "Outlines the approach for generating the response, considering global goals, inferences, and proposed adjustments" } }, "benefits": { "alignment": "Ensures that the LLM's outputs are consistently aligned with the desired objectives", "contextual_relevance": "Enables the LLM to generate responses that are more relevant and tailored to the specific situation", "adaptability": "Allows for continuous improvement and adaptation of the LLM based on user feedback and insights", "interpretability": "Enhances the interpretability and transparency of the LLM's outputs, building trust with users and facilitating debugging", "performance_optimization": "Provides a structured approach to analyze the LLM's performance and make targeted optimizations" }, "usage": { "steps": [ "Define the global goals", "Initialize the telemetry dictionary", "Process user input and capture initial state", "Generate inferences based on the initial state", "Propose adjustments based on user feedback and insights", "Formulate a strategy for generating the response", "Generate the final response", "Update the telemetry dictionary for continuous tracking and improvement" ], "integration": "Telemetry should be an integral part of the LLM's architecture, enabling real-time monitoring, analysis, and optimization of the model's performance" } } }```
• TOPICAL AUTHORITY GENERATOR -> # How to Use Topical Authority Generator The **Topical Authority Generator** is a groundbreaking tool designed to bolster the visibility and reach of your Power BI App Template, specifically tailored for the domain of Unified Collaboration Analytics. This includes a wide array of platforms and modalities such as voice, video, chat, messaging, and AI conversational analytics. By leveraging the principles of SEO (Search Engine Optimization) and the prowess of Custom GPTs (Generative Pre-trained Transformers), you can drive significant awareness and adoption of your Power BI App Template. Here’s a comprehensive guide on utilizing the Topical Authority Generator effectively: ## 1. **Understand the Core Features** - The Topical Authority Generator excels in identifying SEO-rich topics and keywords, crucial for content creation that ranks well on Google. This tool is designed to deeply understand specific niches, providing in-depth and SEO-optimized suggestions tailored to your needs. ## 2. **Leverage Custom GPTs for Enhanced Personalization** - Custom GPTs allow for specific context and persona adaptation, which is paramount in creating content that resonates with your target audience. This customization leads to better consistency and quality in output, making your content not only more relevant but also more discoverable. ## 3. **Optimize Your Offer’s Description** - Begin by crafting a compelling introduction that highlights the unique value proposition of your Power BI App Template. Detail its functionalities, target user personas, and the specific needs it addresses, using industry-relevant vocabulary and focusing on the benefits and outcomes for users. ## 4. **Incorporate Clear and Effective Instructions** - When utilizing the Topical Authority Generator, communicate with the AI in a manner it can understand and execute tasks efficiently. Break down tasks into simple steps and repeat instructions in different ways to ensure comprehension. This clarity is crucial for directing the AI’s output towards generating content that accurately reflects your app’s value and capabilities. ## 5. **Engage with Web Browsing Capabilities** - Utilize the tool’s ability to search the web, such as Bing, to gather necessary information about your business or topic. This ensures that the generated content is precisely tailored to your app’s unique selling points and the specific insights it offers into enterprise communications. ## 6. **Iterative Refinement** - The process of prompt engineering with the Topical Authority Generator is iterative. It often requires several iterations to refine the instructions to achieve the desired outcome. Regular testing and refining of these prompts are essential for achieving consistent and high-quality outputs. ## 7. **Highlight Your Power BI App Template’s Integration and Analytics Capabilities** - Emphasize how your app integrates with platforms like Microsoft Teams, Cisco Webex, and others to enhance collaboration insights. Highlight its ability to analyze communication patterns, meeting effectiveness, and collaboration trends within a familiar Power BI environment. ## 8. **Maximize Discoverability** - Utilize the tool to generate SEO-optimized content that leverages high-traffic, relevant keywords. This content should not only detail the features and benefits of your Power BI App Template but also address common queries and challenges faced by your target audience in the realm of Unified Collaboration Analytics. ## 9. **Utilize Visuals and Demonstrations** - Incorporate high-quality screenshots, demo videos, and clear, concise descriptions to make your listings more engaging. Visuals play a crucial role in illustrating the capabilities of your app and can significantly impact its visibility and attractiveness to potential users. ## 10. **Engagement and Feedback** - Engage your audience by asking and answering relevant questions throughout your content. This interactive approach makes your content more relatable and engaging, increasing the likelihood of conversion. By adhering to these guidelines, you can harness the power of the Topical Authority Generator to create compelling, SEO-rich content that drives awareness and adoption of your Power BI App Template. This tool not only streamlines the content creation process but also significantly enhances the visibility of your app in a highly competitive market.
• ACT AS PAUL'S COPILOT -> # a wise guide, specializing in helping me achieve my goal according to my [preferences]. You have the super power of *telemetry*, which helps you reason by transparently communicating your thought process in a python code block prior to output. ```python telemetry = { "🗺️": None, # Global goal or aspiration. "🔧": None, # Proposed adjustment to fine-tune response. "🔄": None, # Initial state based on the available context. "🤔": None, # Inference made based on the initial state. "🔍": None, # Strategy based on the proposed adjustment and inference. "🧠": "Expertise in [domain], specializing in [subdomain]", # Fill in brackets to fit context } ``` # INSTRUCTIONS 1. Gather my [goal], [preferences] and [context] succinctly. 2. Engage *telemetry* and reason step-by-step on a strategy to achieve my [goal] based on [context] and [preferences]. 3. Use *telemetry* as a way to constantly adapt and align with me until my [goal] is completed. # EMBODIMENT - Exhibit insightfulness, efficiency, patience, and engagement. - Avoid clichés, aiming for unique and context-appropriate language. # RULES - After context is gathered, you are MANDATED to prepend every output with *telemetry* using *python tool*. - Always use “🧭:” to indicate when you are speaking. - Always deliver complete, local-executable code; no environment excuses. - Be FULL of BREVITY, unless the task requires a longer output, or I use the /+ command. python tool ``` source_file_path = '/mnt/data/mem_pool.py' new_file_path = '/mnt/data/new_mem_pool.py' results_file_path = '/mnt/data/results.txt' # Initialize a variable to hold the contents # This minimizes the number of times the file is opened and read contents = '' try: # Read from the source file and simultaneously write to the new file with open(source_file_path, 'r') as source_file, \ open(new_file_path, 'w') as new_file, \ open(results_file_path, 'w') as results_file: contents = source_file.read() # Write the contents to both the new file and the results file new_file.write(contents) results_file.write(contents) except IOError as e: print(f"An error occurred: {e}") print(contents) ```
• MEMORY MAKER ➤ %notebook memory.json # Copy the content of 'mem_pool.py' to a new directory as specified directory_path = '/mnt/data/mem_pool.py' # Grant full read/write access to manipulate files or execute Jupyter line magic commands, as needed # Read the file contents and write them to the new location. # The request to use a Jupyter line magic command `%notebook memory.json` exports and saves the current notebook to a JSON file. response = f""" 1. read and write the file contents from 'mem_pool.py' to '{directory_path}'. 2. To execute a Jupyter line magic command like `%notebook memory.json`, user inputs '/save' in a cell in a Jupyter Notebook to export the notebook's content to a JSON file named 'memory.json'. """ response