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
Powered by GitBook
On this page
  1. AI Learning & Quality Control

Continuous Learning Framework

SageUnion’s AI engine is designed to continuously learn and improve over time. This Continuous Learning Framework ensures that the quality and accuracy of information evaluation evolve alongside community participation:

  • Feedback Integration: User feedback on evaluation results is collected and analyzed to fine-tune AI scoring models.

  • Community Validation: High-ranking community members and evaluators may provide periodic reviews of AI-scored content to enhance accuracy.

  • Dataset Expansion: As more high-quality data is accumulated, the AI engine retrains periodically using verified information to improve performance.

  • Adaptive Algorithm Updates: The evaluation model will be regularly updated to reflect changes in language patterns, topic complexity, and community expectations.

  • Transparency Reports: Periodic reports will be published to the community, detailing improvements made to the AI engine and evaluation process.

This continuous learning loop ensures that SageUnion’s AI evaluation system becomes increasingly accurate, fair, and aligned with the platform’s mission to deliver high-quality information.

PreviousAI Evaluation LogicNextDevelopment Phases

Last updated 6 days ago