Sage Union Whitepaper
  • Introduction
    • Project Overview
    • Problem Statement
    • Vision & Mission
  • Market Overview
    • Current Issues in the Information Ecosystem
    • Potential of Human-AI Collaboration
    • Competitor Analysis & Differentiation
  • SageUnion Solution
    • System Components
    • Human-AI Collaboration Model
    • Definition & Creation of High-Quality Information
  • Platform Architecture
    • Telegram Mini-App Structure
    • Data Collection & Processing Workflow
    • AI-Based Information Evaluation Mechanism
    • Reward Distribution Logic
  • Tokenomics
    • Token Overview (SAGU)
    • Total Supply & Distribution Plan
    • Token Utility & Rewards
    • Revenue Model & Sustainability
  • Governance & Community
    • User Participation Structure
    • Voting & Decision-Making Mechanism
    • Community Incentives
  • AI Learning & Quality Control
    • Data Collection Standards
    • AI Evaluation Logic
    • Continuous Learning Framework
  • Roadmap
    • Development Phases
    • Beta Launch & Official Release Timeline
    • Long-term Vision
  • Team & Partnerships
    • Team Introduction
    • Advisory Board & Partners
    • Strategic Partnerships Plan
  • Conclusion
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  1. AI Learning & Quality Control

AI Evaluation Logic

The AI Evaluation Engine is the core mechanism that determines the quality of user submissions and allocates rewards accordingly. It operates based on the following logic:

  1. Natural Language Processing (NLP) Analysis: The AI utilizes NLP models to analyze the semantic structure, contextual relevance, and factual consistency of each submission.

  2. Scoring Criteria: Each submission is evaluated against multiple weighted criteria:

    • Accuracy (40%)

    • Relevance (20%)

    • Originality (20%)

    • Depth & Detail (10%)

    • Clarity (10%)

  3. Dynamic Adjustments: The AI’s evaluation parameters are dynamically updated based on community feedback, platform growth, and data trends.

  4. Transparency: Users can view their submission scores and understand the reasons behind their evaluation, ensuring trust in the system.

  5. Manual Review (Optional): For edge cases or flagged content, manual human review may be implemented to supplement the AI evaluation process.

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Last updated 6 days ago