What is retrieval-augmented generation (RAG)?

Retrieval augmented generation (RAG) is an AI approach that combines information retrieval with generative AI (GenAI). Instead of relying only on what an AI model learned during training, a RAG system retrieves relevant information from an external source, such as a database, search index, or knowledge base, and uses that information to generate its response.

How does RAG work?

When someone asks a question, the system searches its connected data sources for relevant information. It then provides that information to an AI model, typically a large language model (LLM), which uses it to generate a natural, human-readable response. This allows answers to reflect current or organization-specific information that may have been included in the model’s original training.

Why is RAG important?

GenAI models can sometimes provide outdated or inaccurate information or hallucinate answers. RAG helps reduce this risk by grounding responses in relevant, trusted information. This is particularly valuable for organizations that want AI responses to reflect their own proprietary data, policies, and documentation.

Benefits and use cases

RAG is commonly used for internal knowledge assistants that answer employee questions using company information, customer support tools that reference product manuals and policies, and research assistants that summarize information from a defined set of trusted sources.

Does NexusTek help with RAG?

Yes. NexusTek has nearly 30 years of experience delivering modern IT solutions, including guidance and services that help organizations adopt and manage AI responsibly. We help clients assess readiness, build the right foundation, and put the right governance and support in place so AI investments deliver real business value.