What is Retrieval-augmented generation (RAG)?

Retrieval-Augmented Generation (RAG) is an artificial intelligence (AI) architecture that combines a large language model (LLM) with an external knowledge retrieval system. Instead of relying solely on the model’s training data, a RAG application retrieves relevant information from trusted sources—such as documents, knowledge bases, databases, or vector stores—and uses that information to generate more accurate, relevant, and up-to-date responses.

RAG helps overcome one of the key limitations of standalone language models: their inability to access new or organization-specific information after training. By retrieving current and contextual data at query time, RAG enables AI applications to answer questions using information that was not part of the model’s original training.

Organizations use RAG to power enterprise chatbots, knowledge assistants, customer support platforms, document search, code assistants, and internal AI applications.

How Retrieval-Augmented Generation works

A RAG application retrieves relevant information before the language model generates a response.

This retrieval process enables AI systems to provide responses based on the latest available information rather than relying only on static training data.

Why RAG matters

Many enterprise AI applications need access to internal documents, policies, technical manuals, or business knowledge that changes frequently. Retraining a language model whenever new information becomes available is expensive and time-consuming.

Retrieval-Augmented Generation helps organizations:

• Improve the accuracy of AI responses.
• Reduce AI hallucinations.
• Use current and organization-specific information.
• Enable secure enterprise knowledge search.
• Improve customer support and employee productivity.
• Reduce the need for frequent model retraining.

These advantages make RAG one of the most widely adopted architectures for enterprise generative AI.

Common use cases for RAG

RAG supports a wide variety of AI-powered business applications.

Organizations can customize the knowledge sources to meet their specific business requirements.

How Hexnode supports secure RAG deployments

Hexnode UEM helps organizations secure the endpoints used to access enterprise AI applications and knowledge repositories. Administrators can deploy approved applications, enforce device security policies, manage operating system updates, monitor device compliance, and control application access from a centralized console.

Hexnode UEM also supports application management, device restrictions, and remote security actions across managed devices. These capabilities help reduce endpoint-related risks and strengthen the overall security posture of organizations deploying Retrieval-Augmented Generation applications.