RAG (Retrieval-Augmented Generation) is an AI approach that combines large language models (LLMs) with information retrieval and text generation to produce accurate and contextually relevant responses. It retrieves relevant data from external sources, like documents or databases, and integrates it into generated text.
Examples:
- Customer support: A RAG-powered chatbot retrieves answers from a company’s knowledge base and generates detailed responses for user queries.
- Medical applications: An AI system uses RAG to fetch information from research papers and generate summaries for doctors.
RAG is widely used in scenarios where up-to-date or domain-specific knowledge is essential for accurate outputs.