Retrieval-Augmented Generation, or RAG, continues gaining adoption as organisations seek more reliable ways to use generative AI with enterprise information. RAG systems retrieve relevant information from approved databases, documents, and knowledge repositories before generating responses.

This approach allows businesses to connect AI models with current internal information without requiring organisations to retrain models every time their data changes.

Companies are using RAG for customer support, employee knowledge systems, research, document analysis, and internal search.

Security controls can restrict AI systems to authorised information sources, helping businesses maintain data governance while improving response relevance.

Industry experts expect RAG architectures to remain important for enterprise AI deployments where accuracy, traceability, and access to current information are critical.