> 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/data-collection-standards.md).

# Data Collection Standards

SageUnion is committed to maintaining a **high standard of information quality** within its ecosystem. To achieve this, the platform enforces clear and transparent data collection standards:

* **Relevance:**\
  Submitted data must directly respond to the weekly question and contribute meaningful insights.
* **Accuracy:**\
  Information must be fact-based, verifiable, and free from intentional misinformation.
* **Originality:**\
  Responses must reflect the contributor’s own understanding, analysis, or research, avoiding plagiarism or AI-generated spam.
* **Clarity:**\
  Submissions should be well-structured and easily understandable by other users and the AI evaluation engine.
* **Language Compliance:**\
  The platform supports multilingual submissions but may initially require responses in specific languages to ensure evaluation accuracy.

Before AI evaluation, all user-submitted content undergoes a **preprocessing phase** to remove spam, irrelevant content, and low-effort responses that do not meet these standards.