Decoding RAG

RAG is an approach in NLP that combines large-scale retrieval with neural generative models. The key idea is to retrieve relevant knowledge from large corpora and incorporate that knowledge into the text-generation process. This allows generative models such as Amazon Titan Text, Anthropic Claude, and Generative Pre-trained Transformer 3 (GPT-3) to produce more factual, specific, and coherent text by grounding generations in external knowledge.

RAG has emerged as a promising technique to make neural generative models more knowledgeable and controllable. In this section, we will provide an overview of RAG, explain how it works, and discuss key applications.

What is RAG?

Traditional generative models, such as BART, T5 or GPT-4 are trained on vast amounts of text data in a self-supervised fashion. While this allows them to generate fluent and human-like text, a major limitation is that they lack world knowledge beyond what is contained in their training data. This...