By connecting an AI model to a searchable knowledge base, you give it the ability to look up information rather than rely only on its training data, which can be outdated or incomplete. This approach is practical for customer support, internal documentation, or any domain where you want the AI to cite sources and stay current without constant retraining.
Retrieval-Augmented Generation, or RAG, is a technique where an AI system pulls in relevant documents or data from an external source before generating a response, grounding its answers in specific and up-to-date information.
For everyday users, RAG is the engine behind AI tools that can search your files, reference company documents, or answer questions using your own private knowledge base rather than relying solely on what the model was trained on.
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