Retrieval-Augmented Generation (RAG) is an AI technology that is the combination of information retrieval and generative AI. While traditional systems make use of the information available in the model itself, RAG systems retrieve the information from external sources and give it to the model to generate a response. It leads to generation of more relevant and useful responses, especially in cases when specialized or ever-updating information needs to be used for generating the response. There are two basic parts of RAG technology: retrieving relevant information and generating a response using the retrieved information. RAG systems are capable of retrieving useful information from documents, databases, knowledge bases, and other sources. Such an approach allows companies to develop their own AI applications using their own information without making use of the model’s built-in information.
