The Sequence Opinion #509: Is RAG Dying?
Was this email forwarded to you? Sign up here The Sequence Opinion #509 Is RAG Dying?Long context windows, fine tuning and other trends are challenging the viability of one of the most popular LLM techniques.
Retrieval-Augmented Generation (RAG) is a technique that enhances generative models by integrating a retrieval mechanism, allowing them to access relevant external information. In a RAG pipeline, a query first triggers a search for pertinent documents, often using a vector database or search index. The retrieved text is then fed into the language model to guide its final response. This approach was pioneered around 2020 and quickly became significant for knowledge-intensive AI tasks. It allowed smaller or general-purpose models to achieve state-of-the-art results by incorporating external facts, addressing issues like hallucinations and outdated knowledge. RAG gained widespread adoption, powering numerous research papers and commercial applications. However, with rapid advancements in AI models and architectures, is RAG still as relevant today? Limitations of RAGDespite its strengths, RAG systems introduce several challenges:... 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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