Retrieval-Augmented Generation (RAG) is a technique that combines AI language models with external knowledge sources, allowing the AI to access up-to-date or private information beyond its training data.
RAG enables AI to answer questions about your specific documents, recent events, or proprietary data - making it far more useful for real-world applications.
A company might use RAG to let employees query their internal documentation through an AI assistant, getting accurate answers based on actual company policies rather than generic responses.
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