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.

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