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:

• Access current information beyond their training data cutoff
• Retrieve domain-specific knowledge from company databases, documents, or specialized sources
• Generate contextually accurate responses by grounding outputs in retrieved facts
• Reduce hallucinations through factual grounding

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.