Learn about RAG and improve your knowledge. #education

RAG, or Retrieval-Augmented Generation, is a technique that combines information retrieval and generative AI to produce text incorporating relevant information from external sources. This method involves retrieving data from a knowledge base and using it to generate responses through a generative AI model. An example in healthcare shows how RAG can assist doctors in accessing treatment guidelines quickly and efficiently. The process involves data integration, numerical representation, and access and retrieval of information. RAG has applications in customer service chatbots, automated content creation, and document summarization, with benefits including access to fresher information, relevance and accuracy over time, continuous updates to the knowledge repository, context-rich data, and improvement in understanding and relevance across various domains. Overall, RAG enhances text generation by grounding it in real-world data, improving the quality and efficiency of decision-making processes in different fields.

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