RAG (Retrieval-Augmented Generation) is a technique where AI language models are enhanced with additional, external sources of information. In this process, the AI system’s responses are not only generated from its original training but are also supplemented (augmented) with specifically retrieved current or specialized data.
It can be compared to an expert who doesn’t just speak from memory but consults relevant documents during a conversation. This method significantly improves the accuracy and timeliness of responses, as the system isn’t solely reliant on its “learned knowledge.”
For example, when asked about a company, a RAG system would first check the company’s current business documentation before responding. This reduces the risk of outdated or incorrect information and enables the system to discuss topics that emerged after its original training.
RAG is widely used in businesses today, for instance, to provide more accurate responses to customer inquiries or to make more efficient use of internal documents.