Tag: RAG

Human & Data with AI

Posted on by lechuck park

Data Accumulation Perspective

History → Internet: All knowledge and information accumulated throughout human history is digitized through the internet and converted into AI training data. This consists of multimodal data including text, images, audio, and other formats.

Foundation Model: Large language models (LLMs) and multimodal models are pre-trained based on this vast accumulated data. Examples include GPT, BERT, CLIP, and similar architectures.

Human to AI: Applying Human Cognitive Patterns to AI

1. Chain of Thoughts

  • Implementation of human logical reasoning processes in the Reasoning stage
  • Mimicking human cognitive patterns that break down complex problems into step-by-step solutions
  • Replicating the human approach of “think → analyze → conclude” in AI systems

2. Mixture of Experts

  • AI implementation of human expert collaboration systems utilized in the Experts domain
  • Architecting the way human specialists collaborate on complex problems into model structures
  • Applying the human method of synthesizing multiple expert opinions for problem-solving into AI

3. Retrieval-Augmented Generation (RAG)

  • Implementing the human process of searching existing knowledge → generating new responses into AI systems
  • Systematizing the human approach of “reference material search → comprehensive judgment”

Personal/Enterprise/Sovereign Data Utilization

1. Personal Level

  • Utilizing individual documents, history, preferences, and private data in RAG systems
  • Providing personalized AI assistants and customized services

2. Enterprise Level

  • Integrating organizational internal documents, processes, and business data into RAG systems
  • Implementing enterprise-specific AI solutions and workflow automation

3. Sovereign Level

  • Connecting national or regional strategic data to RAG systems
  • Optimizing national security, policy decisions, and public services

Overall Significance: This architecture represents a Human-Centric AI system that transplants human cognitive abilities and thinking patterns into AI while utilizing multi-layered data from personal to national levels to evolve general-purpose AI (Foundation Models) into intelligent systems specialized for each level. It goes beyond simple data processing to implement human thinking methodologies themselves into next-generation AI systems.

With Claude

Personal(User/Expert) Data Service

Posted on by lechuck park

System Overview

The Personal Data Service is an open expert RAG service platform based on MCP (Model Context Protocol). This system creates a bidirectional ecosystem where both users and experts can benefit mutually, enhancing accessibility to specialized knowledge and improving AI service quality.

Core Components

1. User Interface (Left Side)

  • LLM Model Selection: Users can choose their preferred language model or MoE (Mixture of Experts)
  • Expert Selection: Select domain-specific experts for customized responses
  • Prompt Input: Enter specific questions or requests

2. Open MCP Platform (Center)

  • Integrated Management Hub: Connects and coordinates all system components
  • Request Processing: Matches user requests with appropriate expert RAG systems
  • Service Orchestration: Manages and optimizes the entire workflow

3. LLM Service Layer (Right Side)

  • Multi-LLM Support: Integration with various AI model services
  • OAuth Authentication: Direct user selection of paid/free services
  • Vendor Neutrality: Open architecture independent of specific AI services

4. Expert RAG Ecosystem (Bottom)

  • Specialized Data Registration: Building expert-specific knowledge databases through RAG
  • Quality Management System: Ensuring reliability through evaluation and reputation management
  • Historical Logs: Continuous quality improvement through service usage records

Key Features

  1. Bidirectional Ecosystem: Users obtain expert answers while experts monetize their knowledge
  2. Open Architecture: Scalable platform based on MCP standards
  3. Quality Assurance: Expert and answer quality management through evaluation systems
  4. Flexible Integration: Compatibility with various LLM services
  5. Autonomous Operation: Direct data management and updates by experts

With Claude

AI together!!

Posted on by lechuck park

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:

  1. 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
  2. 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
  3. 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

LLM/RAG/Agentic

Posted on by lechuck park

This image shows a diagram titled “LLM RAG Agentic” that illustrates the components and relationships in an AI system architecture.

The diagram is organized in a grid-like layout with three rows and three columns. Each row appears to represent different functional aspects of the system:

Top row:

  • Left: “Text QnA” in a blue box
  • Middle: A question mark icon with what looks like document/chat symbols
  • Right: “LLM” (Large Language Model) in a blue box with a brain icon connected to various data sources/APIs in the middle

Middle row:

  • Left: “Domain Specific” in a blue box
  • Middle: A “Decision by AI” circle/node that serves as a central connection point
  • Right: “RAG” (Retrieval-Augmented Generation) in a blue box with database/server icons

Bottom row:

  • Left: “Agentic & Control Automation” in a blue box
  • Middle: A task management or workflow icon with checkmarks and a clock
  • Right: “Agentic AI” in a blue box with UI/interface icons

Arrows connect these components, showing how information and processes flow between them. The diagram appears to illustrate how a large language model integrates with retrieval-augmented generation capabilities and agentic (autonomous action-taking) functionality to form a complete AI system.

With Claude

Data with the AI

Posted on by lechuck park

From Claude with some prompting
the key points from the diagram:

  1. Reality of Internet Open Data:
    • Vast amount of open data exists on the internet including:
      • Mobile device data
      • Email communications
      • Video content
      • Location data
    • This open data is utilized by major AI companies for LLM training
    • Key players:
      • OpenAI’s ChatGPT
      • Anthropic’s Claude
      • Google’s Gemini
      • Meta’s LLaMA
  2. Competition Implications:
    • Competition between LLMs trained on similar internet data
    • “Who Winner?” and “A Winner Takes ALL?” suggests potential monopoly in the base LLM market
    • This refers specifically to models trained on public internet data
  3. Market Outlook:
    • While the base LLM market might be dominated by a few players
    • Private enterprise data remains a key differentiator
    • “Still Differentiated and Competitive” indicates ongoing competition through enterprise-specific data
    • Companies can leverage RAG-like technology to combine their private data with LLMs for unique solutions
  4. Key Implications:
    • Base LLM market (trained on internet data) may be dominated by few winners
    • Enterprise competition remains vibrant through:
      • Unique private data assets
      • RAG integration with base LLMs
      • Company-specific implementations
    • Market likely to evolve into dual structure:
      • Foundation LLMs (based on internet data)
      • Enterprise-specific AI services (leveraging private data)

This structure suggests that while base LLM technology might be dominated by a few players, enterprises can maintain competitive advantage through their unique private data assets and specialized implementations using RAG-like technologies.

This creates a market where companies can differentiate themselves even while using the same foundation models, by leveraging their proprietary data and specific use-case implementations.

personalized RAG

Posted on by lechuck park

from Claude with some prompting
This diagram illustrates a personalized RAG (Retrieval-Augmented Generation) system that allows individuals to use their personal data with various LLM (Large Language Model) implementations. Key aspects include:

  1. User input: Represented by a person icon and notebook on the left, indicating personal data or queries.
  2. On-Premise storage: Contains LLM models that can be managed and run locally by the user.
  3. Cloud integration: An API connects to cloud-based LLM services, represented by icons in the “on cloud” section. These also symbolize different cloud-based LLM models.
  4. Flexible model utilization: The structure enables users to leverage both on-premise and cloud-based LLM models, allowing for combination of different models’ strengths or selection of the most suitable model for specific tasks.
  5. Privacy protection: A “Control a privacy Filter” icon emphasizes the importance of managing privacy filters to prevent inappropriate exposure of sensitive information to LLMs.
  6. Model selection: The “Use proper Foundation models” icon stresses the importance of choosing appropriate base models for different tasks.

This system empowers individual users to safely manage their data while flexibly utilizing various LLM models, both on-premise and cloud-based. It places a strong emphasis on privacy protection, which is crucial in RAG systems dealing with personal data.

The diagram effectively showcases how personal data can be integrated with advanced LLM technologies while maintaining control over privacy and model selection.

RAG

Posted on by lechuck park

From Claude with some prompting
This image explains the concept and structure of the RAG (Retrieval-Augmented Generation) model.

First, a large amount of data is collected from the “Internet” and “Big Data” to train a Foundation Model. This model utilizes Deep Learning and Attention mechanisms.

Next, the Foundation Model is fine-tuned using reliable and confirmed data from a Specific Domain (Specific Domain Data). This process creates a model specialized for that particular domain.

Ultimately, this allows the model to provide more reliable responses to users in that specific area. The overall process is summarized by the concept of Retrieval-Augmented Generation.

The image visually represents the components of the RAG model and the flow of data through the system effectively.