What are the pros and cons of RAG (Retrieval-Augmented Generation)?

‍Retrieval-Augmented Generation (RAG) systems offer a unique approach to enhancing natural language processing by combining retrieval and generation techniques. The primary advantage of RAG is its ability to access vast amounts of external knowledge, thereby providing rich contextual information and improving language generation with real-world facts. This means that RAG systems can handle questions and topics they weren't specifically trained on (out-of-distribution queries) better than traditional models, which often struggle with unfamiliar topics.

However, the complexity of managing both the generation model and the retriever component can be challenging, and the reliance on retrieved information might limit the creativity of the generated content. Moreover, implementing RAG systems can be expensive due to the high computational costs associated with frequent retrievals, maintaining large knowledge bases, and processing extensive data for embedding and retrieval purposes. These expenses are compounded by the need for substantial memory and computational power to handle the integration and real-time processing of data from multiple sources.