RAG, or Retriever-Augmented Generation, is an innovative architecture designed for enhancing natural language processing tasks by combining the capabilities of two main components: a retriever and a generator.

Information Retrieval Meets Generation

• • Retriever Component: This part is responsible for fetching relevant information or documents from a large dataset that could help in generating accurate and contextual answers.
• • Generator Component: Based on the information retrieved, this part generates a coherent and contextually relevant answer or content, enhancing the quality and accuracy of responses.

RAG architecture bridges the gap between retrieving factual information and generating human-like responses, making it a powerful tool for applications like question answering, chatbots, and more.