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Retrieval-Augmented Generation (RAG) is a technique that combines an AI language model with a private knowledge base, allowing the AI to answer questions using your own documents, data, or content rather than relying solely on its training data. Instead of the AI guessing from general knowledge, RAG retrieves the most relevant information from your files first, then uses it to generate an accurate, grounded answer. For businesses, RAG makes it possible to build AI assistants that know your products, policies, prices, and procedures. A customer service bot built with RAG can answer questions based on your actual FAQ and product documentation. An internal tool built with RAG can help employees search HR policies or project history in seconds. It is one of the most practical applications of AI for SMBs that have valuable knowledge locked in documents.