Retrieval-Augmented Generation (RAG) combines large language models (LLMs) like ChatGPT with an information retrieval system. RAG allows you to 'ground' the data for generative AI, by incorporating external knowledge, ensuring relevance and security. Vector databases populated by embeddings allows relevant information to be supplied to GPT models, making it ideal for intelligent applications and content generation.