User-Guided Model Training

Ruvi isn’t just a platform for using AI—it’s a platform for shaping it. Through a unique feature called User-Guided Model Training, Ruvi invites its community to play an active role in training and improving its native AI models. In return, users earn $RUVI tokens, reinforcing a circular, decentralized system where contributions are rewarded and innovation is community-driven.

This feature opens up a new layer of engagement and transparency rarely seen in traditional AI platforms.

🧠 How It Works

Ruvi integrates mechanisms that allow users to interact with AI outputs in ways that generate valuable training data. These include:

Rating & Feedback Users can rate AI outputs (e.g., “Was this answer helpful?”) and submit corrections or suggestions to improve future responses.
Data Annotation Tasks Users can opt in to label data—such as identifying objects in images, classifying content, or tagging sentiment in text.
Prompt Engineering Challenges Periodic challenges where users compete to create the best-performing prompts for specific tasks (e.g., best script for an ad), with top entries rewarded and used to improve the prompt-tuning layer of Ruvi’s models.
Content Contributions Verified users may contribute text, images, or other datasets for model fine-tuning, governed by community-approved standards.

💎 Token Rewards System

Every valid contribution is tracked and assigned a reward score based on:

Value to the training process
Uniqueness or quality of the input
Community validation (peer reviews, upvotes)

Participants are rewarded in $RUVI tokens, which can be used within the platform or staked for additional benefits.

🤝 Community-Led AI

Eventually, model updates and training priorities can be governed via a decentralized voting system. Token holders will be able to propose and vote on:

New datasets to integrate
Model fine-tuning directions
Ethical use policies
Which templates or models to prioritize

This transforms Ruvi into a living, evolving AI ecosystem that reflects the needs and values of its users—not just a central development team.

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Last updated 2 months ago

🧠 How It Works

Ruvi integrates mechanisms that allow users to interact with AI outputs in ways that generate valuable training data. These include:

Rating & Feedback Users can rate AI outputs (e.g., “Was this answer helpful?”) and submit corrections or suggestions to improve future responses.

Data Annotation Tasks Users can opt in to label data—such as identifying objects in images, classifying content, or tagging sentiment in text.

Prompt Engineering Challenges Periodic challenges where users compete to create the best-performing prompts for specific tasks (e.g., best script for an ad), with top entries rewarded and used to improve the prompt-tuning layer of Ruvi’s models.

Content Contributions Verified users may contribute text, images, or other datasets for model fine-tuning, governed by community-approved standards.

🤝 Community-Led AI

Eventually, model updates and training priorities can be governed via a decentralized voting system. Token holders will be able to propose and vote on:

New datasets to integrate

Model fine-tuning directions

Ethical use policies

Which templates or models to prioritize

This transforms Ruvi into a living, evolving AI ecosystem that reflects the needs and values of its users—not just a central development team.