In plain English
RAG stands for retrieval-augmented generation. Instead of relying only on what a model learned during training, a RAG system first looks up relevant information from an outside source and then gives that information to the model to help produce a more grounded response.
Example
A company chatbot could search internal policy documents for the section related to parental leave and pass those results to an AI model before it answers an employee's question.