What is Retrieval Augmented Generation (RAG)?
Retrieval Augmented Generation is a technique that enhances AI model responses by combining generative capabilities with real-time information retrieval. Instead of relying solely on pre-trained knowledge, RAG systems first search through specific document repositories or knowledge bases to find relevant information, then use that retrieved content to generate accurate, contextually grounded responses. In financial services, this allows AI systems to reference up-to-date regulatory guidance, product literature or client records when generating advice summaries or compliance documentation, reducing the risk of outdated or fabricated information.
Retrieval Augmented Generation (RAG) in financial services
For financial services firms, RAG means an AI answer can be grounded in approved material — policies, product documents or regulatory guidance — rather than a model's general knowledge. That makes outputs easier to verify and helps keep them consistent with the firm's own standards.