Retrieval-Augmented Generation (RAG) is an AI approach that enhances a Large Language Model (LLM) by grounding its responses in external, real-time data. Instead of relying only on its training data, a RAG system retrieves relevant information from sources such as databases, documents, or knowledge bases and passes it to the LLM before generating an answer. This process improves accuracy, keeps responses up to date, and reduces the likelihood of AI “hallucinations.” RAG is especially valuable for digital experiences like onsite search, knowledge bases, and customer support chatbots. By combining retrieval with generation, RAG enables more specific, trustworthy, and context-aware responses for users.
