> For the complete documentation index, see [llms.txt](https://sageunion.gitbook.io/sageunion/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sageunion.gitbook.io/sageunion/ai-learning-and-quality-control/ai-evaluation-logic.md).

# 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.