Retrieval-Augmented Generation glossary definition

Retrieval-Augmented Generation (RAG) is an artificial intelligence method that allows a language model to search for and retrieve information from external sources before generating a response. This allows AI to rely on documents, databases, or Web content rather than solely on knowledge acquired during its training.

How does RAG work?

When a question is asked, the system first searches its available sources for the most relevant information. This information is then provided to the AI model as additional context to help it generate a more relevant and better-informed response.

Why use Retrieval-Augmented Generation?

RAG allows AI systems to use recent, specialized, or organization-specific information. It is commonly used in AI agents, conversational assistants, and some AI-powered search engines.

In summary, RAG allows an AI system to retrieve relevant information from external sources and use it to enrich the responses it generates.

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