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Retrieval-Augmented Generation: Empower Static Models

Artificial intelligence has come a long way from rule-based systems to sophisticated models capable of understanding and generating human-like text. Yet, despite these advancements, a fundamental limitation persists: traditional AI models rely heavily on the data they were trained on, which can quickly become outdated. Enter Retrieval-Augmented Generation (RAG) - a paradigm that enhances AI models by allowing them to access and incorporate up-to-date information during inference. This article explores how RAG works, its significance in the evolution of AI, and its practical applications that are reshaping various industries.