# 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://openledger.gitbook.io/openledger/datanets-and-proof-of-attribution/rag-attribution.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.