Source Reference
A RAG (Retriever-Augmented Generation) AI system is designed to enhance the language model's ability to provide more accurate and contextually relevant answers by integrating external information sources. Here's a general overview of how it works:
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Question Parsing: Initially, when a question or query is inputted into the system, the AI parses it to understand the context and the specific information being requested.
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Document Retrieval: The system then retrieves relevant documents or information from an external database or knowledge base. This retrieval is typically based on the relevance of the content in the documents to the query. For instance, if the question is about a specific historical event, the system will pull up documents related to that event.
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Context Integration: The retrieved documents are then used to augment the AI's existing knowledge. This step is crucial as it allows the AI to incorporate up-to-date or specific information that it might not have in its pre-trained data.
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Answer Generation: The AI combines its pre-trained knowledge with the information from the retrieved documents to generate a response. This response is not just based on the AI's training but is also informed by the external sources, making it more accurate and contextually relevant.
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Source Referencing: Finally, and importantly, the system includes references to the sources of the external context used in the answer. This is often done by citing the document titles, URLs, or specific excerpts used to construct the response. This transparency is crucial for verifying the information and understanding the basis of the AI's response.
In summary, a RAG AI system provides more nuanced and informed responses by leveraging external information sources, and it enhances the trustworthiness and verifiability of its answers by explicitly referencing these sources.
