Chin Keong Lam

This talk introduces Knowledge Augmented Generation (KAG) as an enhancement over traditional Retrieval Augmented Generation (RAG). KAG integrates structured knowledge graphs with large language models to enable more accurate, insightful, and decision-driving responses. The core thesis is that by representing wisdom as interconnected knowledge, AI systems can move beyond simple data retrieval to understanding and strategic advisory roles, particularly in complex domains.