Standard LLMs are limited by their training data: they don't know about events after their training cutoff, and they don't have access to an organization's internal documents, databases, or proprietary knowledge. RAG addresses this by pairing the model with a retrieval layer, typically a vector database, that searches relevant documents at query time and provides them as context for the model's response.
RAG has become one of the most common enterprise AI architectures because it allows organizations to deploy AI assistants over their own data without the cost, complexity, and data governance risks of fine-tuning. An employee asking an AI assistant about company policy, a customer service agent referencing a product knowledge base, a sales tool surfacing case studies, all of these can be built on RAG.
But RAG doesn't eliminate data governance concerns; it shifts them. The retrieval layer indexes and queries real enterprise data, which may include personal information, confidential records, or data collected under specific consent conditions. Organizations deploying RAG need to ensure the retrieval layer respects data access controls, that personal data in indexed documents is handled consistently with applicable privacy rights, and that data used in RAG responses is tracked for auditability.