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Rag rag retrieval augmented generation has become the most popular llm application solution and opening method. It is not difficult to understand, just retrieve relevant information through its own vertical database. Rag retrieval augmented generation, referred to as rag, has become the most popular LLM application solution at present. It’s not difficult to understand. It just retrieves relevant information through its own vertical database and then merges it into a prompt module. Rag and agent are still on the rise, with clear opportunities especially in enterprise services, vertical industries, and scenarios that combine software and hardware. For practitioners, they need: Technical roots: In-depth understanding of the underlying mechanisms of large models (such as attention calculations, fine-tuning strategies). field.
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To this end, researchers have proposed adaptive rag methods, which allow the rag system to automatically decide when to perform "retrieval enhancement" and when to only use the large model itself to output results. This answer is based on the analysis of four representative rag methods recently proposed.
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