RAG has fast become a darling adjunct to the generative AI services. Still highly applicable (some say essential) as a means of bringing in domain-specific (or indeed business- or even department-specific) smaller language model context to the wider world of large language models, RAG is something we should never start ragging (i.e. dissing) on. RAG uses more specifically aligned enterprise data to feed relevant information into an AI model or agent to improve the quality of the generated response. By incorporating this more “finely tailored data” at the point of query, a RAG architecture can increase the relevance and factual accuracy of AI outputs. RAG-powered models are able to reduce frustrating hallucinations and ground responses in contextually relevant information. “However, when integrated with a RAG layer that searches a current database of workflows, banking assets and past queries, the assistant can pull in new, relevant protocols based on user questions and explain them back in natural language. RAG should also be backed up by a ‘fast data layer’ that aggregates and structures the unstructured data within an organization, which the RAG architecture can then parse through when queried. That’s RAG at work. RAG closes the gap between enterprise AI deployment and success by situating model results within appropriate, helpful business context.