RAG Attribution

Understanding Retrieval-Augmented Generation (RAG) Attribution

Retrieval-Augmented Generation (RAG) combines generative AI with retrieval-based data sources, ensuring that model outputs are both accurate and traceable. In OpenLedger, RAG Attribution ensures that:

• Data Provenance is Maintained: Every piece of retrieved information used in generating an output is verifiably linked to its source.

• Contributors are Rewarded: Data providers receive attribution-based incentives based on how frequently their data is retrieved and utilized.

• Transparency is Ensured: Users can trace model outputs back to the datasets that influenced them, reducing misinformation risks.

RAG Attribution Pipeline

Step 1: Query Processing & Data Retrieval

• A user submits a query to an AI model.

• The model retrieves relevant data from indexed sources in the OpenLedger data reservoir.

Step 2: Attributed Data Usage

• Retrieved information is incorporated into the model’s response.

• All utilized data points are cryptographically logged for attribution tracking.

Step 3: Contributor Attribution & Rewards

• Data contributors receive micro-rewards each time their data is retrieved and used.

• Attribution-based incentives scale with data relevance and query frequency.

Step 4: Transparent Citations in Model Outputs

• Model responses include citations or metadata pointing to the original data sources.

• Users can verify where generated insights originate from, ensuring accountability and trust.