RAG, or retrieval-augmented generation, is an architecture utilized in chatbots to enhance reliability by utilizing a publisher's archives. Unlike general-purpose chatbots that scour a wide array of sources, RAG systems focus on a journalist-defined database to retrieve pertinent information. This method not only generates responses based on specific datasets, such as a newsroom's articles or legal documentation, but also ensures that answers are fact-checked and properly attributed. The ability to filter through large amounts of data reveals important elements that may otherwise be overlooked by journalists.
RAG, or retrieval-augmented generation, enhances the reliability of chatbots by allowing them to leverage a publisher's archives for answering reader inquiries.
This system retrieves information from a defined database rather than relying on disparate online content, ensuring factual accuracy and appropriate citations.
A RAG-powered model not only generates responses but augments them with reliable information and attributions, making it essential for journalists in the reliability business.
By filtering through vast datasets, RAG systems help reveal critical insights that journalists might overlook, described as "smelling the data" by a Norwegian journalist.
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