The primary objective of this research is to discuss the applications of Retrieval-Augmented Generation(RAG)technology in AI scenarios of media convergence.RAG technologies enhance the output of large language models by retrieving information from external knowledge bases,thereby improving the accuracy,diversity,and timeliness of generated content.This paper delves into the principles underlying RAG techniques and their optimization strategies,alongside examining their implementation in various media convergence scenarios such as media knowledge repositories,large model-powered online customer service,content creation assistance,and AI-driven shopping guides.
维普
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