This diagram titled “AI together!!” illustrates a comprehensive architecture for AI-powered question-answering systems, focusing on the integration of user data, tools, and AI models through standardized protocols.
Key Components:
- Left Area (Blue) – User Side:
- Prompt: The entry point for user queries, represented by a UI interface with chat elements
- RAG (Retrieval Augmented Generation): A system that enhances AI responses by retrieving relevant information from user data sources
- My Data: User’s personal data repositories shown as spreadsheets and databases
- My Tool: Custom tools that can be integrated into the workflow
- Right Area (Purple) – AI Model Side:
- AI Model (foundation): The core AI foundation model represented by a robot icon
- MOE (Mixture Of Experts): A system that combines multiple specialized AI models for improved performance
- Domain Specific AI Model: Specialized AI models trained for particular domains or tasks
- External or Internet: Connection to external knowledge sources and internet resources
- Center Area (Green) – Connection Standard:
- MCP (Model Context Protocol): A standardized protocol that facilitates communication between user-side components and AI models, labeled as “Standard of Connecting”
Information Flow:
- Questions flow from the prompt interface on the left to the AI models on the right
- Answers are generated by the AI models and returned to the user interface
- The RAG system augments queries with relevant information from the user’s data
- Semantic Search provides additional connections between components
- All interactions are standardized through the MCP framework
This architecture demonstrates how personal data and custom tools can be seamlessly integrated with foundation and specialized AI models to create a more personalized, context-aware AI system that delivers more accurate and relevant responses to user queries.
With Claude
Lechuck Park 