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Yin-Yang: LLM vs Knowledge Graph

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Description

This MicroSim uses the ancient Yin-Yang symbol to illustrate the complementary nature of two important technologies in modern AI and data management: Large Language Models (LLMs) and Enterprise Knowledge Graphs.

The Yin-Yang symbol represents the concept of dualism, where two opposite forces are interconnected and interdependent. Similarly, LLMs and Knowledge Graphs serve different but complementary purposes in the technology landscape:

Large Language Models (Yang - White):

  • Models of language
  • Contains knowledge of the outside world
  • Designed to predict the next token
  • Statistical in nature
  • Likely to hallucinate

Enterprise Knowledge Graphs (Yin - Black):

  • Models of your customers, products, and workflows
  • Contains internal organizational knowledge
  • Designed for knowledge traversal
  • Deterministic in nature
  • Provides precise graph queries

Learning Objectives

After exploring this MicroSim, students should be able to:

  1. Understand the fundamental differences between Large Language Models and Knowledge Graphs
  2. Recognize the complementary nature of these two technologies
  3. Identify appropriate use cases for each technology
  4. Analyze how these technologies can work together in enterprise applications
  5. Evaluate the strengths and limitations of each approach

Lesson Plan

Introduction (5 minutes)

Introduce the concept of the Yin-Yang symbol and its representation of complementary opposites. Ask students:

  • What technologies do they use for information retrieval?
  • Have they interacted with chatbots or AI assistants?
  • Do they know how their organization stores and manages data?

Exploration (10 minutes)

Have students examine the MicroSim and discuss:

  1. Compare and Contrast: What are the key differences between the two technologies?
  2. Statistical vs Deterministic: What does it mean for LLMs to be "statistical" versus Knowledge Graphs being "deterministic"?
  3. Hallucination vs Precision: Why might an LLM hallucinate, while a Knowledge Graph provides precise queries?
  4. Knowledge Scope: How does the type of knowledge differ between these technologies?

Discussion Questions (10 minutes)

  1. When would you choose an LLM?
  2. Open-ended questions
  3. Natural language understanding
  4. General knowledge tasks

  5. Creative content generation

  6. When would you choose a Knowledge Graph?

  7. Structured data queries
  8. Relationship traversal
  9. Mission-critical accuracy

  10. Internal organizational knowledge

  11. How might these technologies complement each other?

  12. LLMs can provide natural language interfaces to Knowledge Graphs
  13. Knowledge Graphs can ground LLM responses with factual data
  14. LLMs can help build and populate Knowledge Graphs
  15. Knowledge Graphs can provide context to improve LLM accuracy

Application (10 minutes)

Have students identify real-world scenarios where:

  1. An LLM would be the better choice
  2. A Knowledge Graph would be the better choice
  3. Both technologies working together would be ideal

Example scenarios: - Customer service chatbot - Medical diagnosis system - Product recommendation engine - Enterprise search system - Financial fraud detection

Key Takeaways

  1. Complementary Not Competitive: LLMs and Knowledge Graphs serve different purposes and are most powerful when used together
  2. Statistical vs Deterministic: Understand the fundamental difference in how these technologies process and return information
  3. Use Case Matters: Choose the right tool for the right job based on requirements for accuracy, structure, and knowledge scope
  4. Hybrid Approaches: Modern AI systems often combine both technologies to leverage their respective strengths

Extension Activities

  1. Research Project: Investigate a real-world application that combines LLMs and Knowledge Graphs
  2. Design Challenge: Design a system for your school or organization that uses both technologies
  3. Debate: Organize a debate on scenarios where one technology might be superior to the other
  4. Technical Deep Dive: Explore how Graph RAG (Retrieval Augmented Generation) combines these technologies

Prerequisites

  • Basic understanding of databases and data structures
  • Familiarity with AI and machine learning concepts
  • Understanding of what makes information "structured" vs "unstructured"
  • Retrieval Augmented Generation (RAG)
  • Graph RAG
  • Semantic Search
  • Natural Language Processing
  • Graph Database Technologies
  • Vector Databases
  • Hybrid AI Systems

References

Technical Notes

This MicroSim is built using p5.js and follows the MicroSim design pattern with:

  • Width-responsive design that adapts to different screen sizes
  • Clean separation of drawing area and information display
  • Accessible design with screen reader support
  • No external dependencies beyond p5.js

The visualization uses the traditional Yin-Yang symbol with its characteristic S-curve dividing the circle, with small circles representing that each contains an element of the other (showing their interconnected nature).