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.
Retrieval-augmented generation, RAG, is an AI technique that retrieves relevant information from a trusted data source and feeds it to a generative model so the answer is grounded in real, current data rather than the model's memory alone. It reduces made-up answers and lets AI respond from a company's own knowledge. In CRM, RAG is how AI can answer accurately from your actual records and documents.
Frequently Asked Questions
An AI technique that retrieves relevant information from a trusted source and feeds it to a generative model, so answers are grounded in real, current data.