The Sequence Knowledge #468: A New Series About RAG
Was this email forwarded to you? Sign up here The Sequence Knowledge #468: A New Series About RAGExploring key concepts of one of the most popular methods in generative AI solutions.
Today we will Discuss:
💡 ML Concept of the Day: A New Series About Retrieval Augmented Generation(RAG)As our first new series of 2025, we would like to cover one of the simplest but most active areas in generative AI. We are talking about retrieval augmented generation or how we often refer to it: RAG. Conceptually, RAG is an architectural framework that enhances the functionality of large language models (LLMs) by incorporating external data retrieval mechanisms. This integration allows LLMs to access real-time, relevant information, thereby addressing the limitations of traditional generative models that rely solely on static training data. By retrieving pertinent documents or data points in response to specific queries, RAG ensures that the generated outputs are not only contextually appropriate but also factually accurate, significantly reducing the incidence of outdated or erroneous information. This capability is particularly beneficial in applications such as customer support and knowledge management, where timely and precise responses are critical... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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