Retrieval-Augmented Generation (RAG) is a technique that enhances large language models by combining their generative capabilities with external knowledge retrieval, allowing them to produce more accurate, up-to-date, and context-aware responses. Instead of relying solely on information stored during training, a RAG system first retrieves relevant documents from a database or search index—an approach explained in detail here:
https://www.pinecone.io/learn/retrieval-au[...] and then uses those documents as grounding for generation, which helps reduce hallucinations and improves trustworthiness.