Scaling RAG: Architectural Considerations for Large Models and Knowledge Sources
Retrieval-Augmented Generation (RAG) is a cutting-edge strategy that combines the strengths of retrieval-based and generation-based models. In RAG, the model retrieves relevant documents or information from a vast knowledge base to enhance its response generation capabilities. This hybrid method leverages the power of large language models, like BERT or GPT, to generate coherent and contextually appropriate responses while grounding these responses in concrete, retrieved data.
