RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is an AI approach that retrieves relevant information from external data sources and provides it to a generative AI model as context for producing a response.
What is RAG?
RAG combines information retrieval with text generation. When a user submits a query, the system searches a connected knowledge base or other data source for relevant information, then supplies the retrieved content to the AI model to help generate a response grounded in that information.
Why is RAG Important?
RAG can improve the accuracy and relevance of AI responses by giving models access to current, domain-specific, or private information without requiring the model to be retrained. It can also help organizations build AI applications around proprietary knowledge and controlled data sources.
Common use cases
RAG is commonly used for enterprise search, document-based question answering, customer support, knowledge assistants, research applications, and AI systems that need access to frequently changing information.