RAG
Also known as: Retrieval-Augmented Generation
RAG (Retrieval-Augmented Generation) is a technique where AI models retrieve relevant documents or data before generating responses.
Full Explanation
RAG (Retrieval-Augmented Generation) is a technique where AI models retrieve relevant documents or data before generating responses. Instead of relying solely on training data, RAG-enabled systems (like Perplexity, Google AI Overviews, and enterprise chatbots) fetch current information from indexed sources. For brands, this means your content can be "retrieved" and cited in real-time AI responses, even if it was published after the model was trained.
Example
When Perplexity answers a question about your product, it retrieves and cites your webpage content in real-time via RAG, even if that content was published yesterday.
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