Retrieval-Augmented Generation (RAG) enhances large language models by retrieving relevant information from external data sources before generating responses.

Traditional large language models generate answers based solely on the data they were trained on, which becomes outdated over time. RAG introduces an information retrieval step that pulls in up-to-date and domain-specific knowledge from external databases or knowledge bases. This fresh context is combined with the user query to produce more accurate and relevant AI outputs, making the generated content grounded and less prone to errors or hallucinations.