Definition
Retrieval-augmented generation, or RAG, is an AI technique that enhances large language models by connecting them to external knowledge sources. It works by first retrieving relevant information from a database or document collection and then using that information to inform the language model's response. This approach significantly improves the accuracy and relevance of AI-generated text, making it invaluable for tasks requiring up-to-date or specialized knowledge. For example, a RAG system could help a customer service chatbot access product manuals to answer complex user queries. Unlike pure generative models that rely solely on their training data, RAG grounds responses in verifiable external information. You will encounter RAG concepts in advanced AI and cloud architecture discussions, particularly relevant for AWS certifications.