Retrieval Augmented Generation (RAG)

RAG is a hybrid artificial intelligence approach that combines the strengths of pre-trained language models with information retrieval systems to enhance generation quality. This approach assumes including the external information in the prompt effectively expanding its knowledge base beyond what it was initially trained on.

RAG first uses a retriever component to fetch relevant documents or data from a large corpus based on the input query or context. It then passes this retrieved information along with the original input to a generator model, synthesizing the inputs to produce a coherent and contextually informed response.

One of the key benefits of RAG is its ability to stay up-to-date with the latest information and facts, as it can retrieve from the most recent documents in its corpus. This makes it particularly useful for applications where the freshness of information is crucial, such as news summarization or real-time Q&A systems.

Additionally, RAG provides more explainable AI outputs, as the sources of its generated content can be traced back to the specific documents it retrieved during the generation process. This transparency is beneficial for critical applications where understanding the basis of the AI's response is essential.