What is RAG | Generative AI Tutorial for Beginner | Gen AI | ChatGPT Tutorial [Updated 2024]-igmGuru

To Know More, Visit: https://www.igmguru.com/machine-learning-ai/generative-ai-training/ In generative AI, "RAG" refers to "Retrieval-Augmented Generation." It's an architecture that integrates both retrieval and generation techniques to enhance the quality and relevance of generated content. Here's how RAG typically works: 1. Retrieval Component: This part involves retrieving relevant information from a large corpus or knowledge base given a prompt or context. The retrieval process aims to find the most relevant passages or documents that contain information related to the input. 2. Generation Component: After retrieving relevant information, the generation component synthesizes new text based on the retrieved content and the input prompt. This generation process allows the model to produce coherent and contextually relevant responses. By combining retrieval and generation, RAG models can address the limitations of purely generative approaches, such as maintaining coherence and relevance, while also leveraging the vast amount of information available in large corpora. RAG architectures have been applied to various natural language processing tasks, including question answering, text summarization, and dialogue generation, where generating responses grounded in specific knowledge or contexts is crucial.