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Microsoft researchers propose framework for building data-augmented LLM applications
From explicit fact retrieval to hidden rationales, all you need to know about data-augmented LLM applications.
To address this complexity, the researchers propose a four-level categorization of user queries based on the type of external data required and the cognitive processing involved in generating accurate and relevant responses: For example, at the indexing stage, where the RAG system creates a store of data chunks that can be later retrieved as context, it might have to deal with large and unstructured datasets, potentially containing multi-modal elements like images and tables. “Navigating hidden rationale queries… demands sophisticated analytical techniques to decode and leverage the latent wisdom embedded within disparate data sources,” the researchers write.
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