Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG ) is an AI architecture that enhances large language models by combining their generative capabilities with real-time information retrieval from external knowledge bases or databases.

In the AI agent ecosystem, RAG enables agents to:

RAG works by first retrieving relevant information from vector databases or knowledge repositories, then using that context to inform the AI agent's response generation. This makes AI agents more reliable for enterprise applications, customer support, and knowledge-intensive tasks where accuracy and up-to-date information are critical.

Related terms

• Agentic Retrieval-Augmented Generation: a survey on agentic RAG
• C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
• Hire Retrieval-Augmented Generation (RAG) developers
• IM-RAG (Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues)
• RAG (Retrieval-Augmented Generation)

Vstorm builds production systems that use Retrieval-Augmented Generation: RAG development service, Agentic AI consulting.

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