What is RAG?

Retrieve-Augment-Generate (RAG) is a framework for natural language understanding and generation tasks that combines three key components to enhance the quality and relevance of generated text.

1. Retrieve: The first step involves retrieving relevant information from a large corpus of text, such as web articles or documents. This is typically done using a search engine or an information retrieval system. The retrieved documents act as a knowledge base for the subsequent steps.

2. Augment: After retrieving relevant documents, the next step is to augment this information. This involves extracting key facts, concepts, or entities from the retrieved text. These augmentations help in organizing and structuring the retrieved knowledge, making it easier for the system to generate coherent and contextually relevant responses.

3. Generate: The final step is the generation of natural language text based on the augmented knowledge. This can include tasks like answering questions, summarizing information, or even creative text generation. By combining the retrieved knowledge with the augmented context, the generated text is more informative and contextually appropriate.

RAG is a powerful framework used in various natural language processing applications, such as question-answering systems, chatbots, and content generation, as it leverages both existing knowledge and the ability to generate human-like text, resulting in more accurate and context-aware responses.