What is Retrieval-Augmented Generation?

Retrieval-augmented generation, or RAG, is an AI technique that combines a language model with a live source of trusted information so responses are grounded in real, current data rather than only the model's training. When a question comes in, the system first retrieves relevant facts, such as a dealership's current inventory, pricing, hours, or policies, and then uses the language model to craft an accurate, natural answer based on that retrieved information. For dealerships, RAG is important because it lets an AI assistant answer questions about actual vehicles in stock or specific store details reliably, instead of guessing. This reduces the risk of an AI giving outdated or incorrect information to a customer. RAG-based tools stay useful as data changes because they always pull the latest information, making them well suited to fast-moving inventory and pricing in automotive retail.