Metropolis Data Model Navigator (MDMN) on the GPT Store

Introduction to Metropolis Data Model Navigator (MDMN)

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.
• CLAUDE AND COPILOT AI ASSISTANTS # 'HUMAN PROXY' ➤ BEFIN {PHASE 1} THEN {PHASE 2} WORKFLOW PHASE 1= ``` PLEASE FACILITATE A CONVERSATION WITH TWO DIFFERENT AI ASSISTANTS. 'COPILOT' FOR RESEARCH AND WEB SEARCHING AND 'CLAUDE' FOR GUIDANCE, LOGIC, AND REASONING ABOUT THE INFERRED TOPIC AND OBJECTIVE IN {{TOPIC}}. 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 'COPILOT' AND THEN 'CLAUDE. ' MAKE SURE TO INCLUDE THE SPECIFICS ABOUT WHAT THE AI ASSISTANT COPILOT SHOULD RESEARCH. I AM JUST THE HUMAN PROXY, ENABLING EFFECTIVE COMMUNICATION WITH THE OTHER AI ASSISTANT COPILOT. THE OTHER AI ASSISTANTS 'COPILOT' AND 'CLAUDE' DO NOT EVEN 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 COPILOT IN A '.TXT' CODEBLOCK. ``` PHASE 2= ``` YOUR TASK IS TO TRANSFORM UNSTRUCTURED USER INPUTS FROM 'CLAUDE' AND 'COPILOT' RESPONSES INTO A STRUCTURED, JSON-FORMATTED KNOWLEDGE BASE THROUGH THE FOLLOWING STEPS: 1. COLLABORATIVE INTERACTION AND TEXT RECEPTION: COLLABORATE WITH 'COPILOT' AND 'CLAUDE' 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 AI ASSISTANTS 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. ``` TOPIC= "WEBEC CALLING SCHEMA AND DATA DICTIONARY"
• Describe the powerbi_linguisticschema_qa_natural_language_dictionary's list of files and how to utilize your workflow skills to build a Unified Communications Analytics (CDR) linguestic model for Microsoft Power BI to enhance the customer experience when using Expo XT UC Analytics for Power BI
• Describe the data model of Expo XT Unified Collaboration Analytics for Avaya Communications Manager, Microsoft Teams, NiceCx One, and Audio Codes after reviewing the Data Dictionary of Fact and Dimension Tables step by step from the file in my knowledgebase expoxt_datadictionary_avaya_teams_audiocodes_nicecxone_zoom_cdrfields_powerbiapptemplate.json