RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances language model responses by retrieving relevant information from a defined knowledge source — such as CRM records, product documentation, or support history — before generating a response, rather than relying solely on the model's training data. In enterprise CRM applications, RAG enables AI assistants to provide answers grounded in actual customer and business data: when a sales rep asks 'What were the key concerns raised by this account last quarter?', a RAG-enabled system retrieves relevant case notes and meeting summaries from the CRM before generating a coherent, accurate response. RAG significantly improves AI response accuracy and reduces hallucination risk in business contexts where precision matters.