RAG

Retrieval-Augmented Generation (RAG) is a technique that enhances AI responses by combining language models with external knowledge bases. Instead of relying solely on training data, RAG systems first retrieve relevant information from documents or databases, then use that context to generate more accurate, up-to-date, and verifiable responses. This approach reduces hallucinations and enables AI to access company-specific or recent information not in its training data.