Retrieval-Augmented Generation (RAG)

A hybrid AI framework that combines retrieval-based and generative models.

In RAG, the system retrieves relevant information from a large dataset (retrieval phase) and uses it to generate coherent and contextually accurate responses (generation phase).

This approach enhances AI agents by allowing them to access a broader knowledge base while maintaining the ability to produce detailed, informative outputs.

It’s particularly useful for tasks like answering complex questions and improving accuracy in conversational agents.