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This podcast covers Knowledge Augmented Generation (KAG), a novel AI framework designed to improve Large Language Models (LLMs) in professional fields.
KAG addresses limitations of existing retrieval-based systems by incorporating knowledge graphs and advanced logical reasoning capabilities. This allows for more accurate and contextually relevant responses, particularly in complex, multi-step queries.
The framework comprises three core components: KAG-Builder, KAG-Solver, and KAG-Model, working together to process information and generate professional-level outputs.
The system's efficacy is demonstrated through successful applications in e-government and e-health, significantly outperforming traditional AI systems in accuracy and logical reasoning.

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