Wisdom-Driven Knowledge Augmented Generation at Scale - Chin Keong Lam, Patho AI
Aug 22, 2025 · 18:43
Chin Keong Lam, founder and CEO of Patho.ai, argues that Wisdom-Driven Knowledge Graphs (KAG) significantly outperform traditional RAG systems for complex quantitative analysis and expert-level advice. He presents a wisdom-decision-situation feedback loop where knowledge, experience, and insight feed into wisdom, mapped to a practical competitive analysis chatbot for a marketing client. The system uses N8n and a multi-agent architecture with a supervisory Wisdom Agent overseeing insight, knowledge, and strategy agents, all updating a centralized knowledge graph. Lam cites five reasons knowledge graphs beat RAG: capturing complex relationships, improved accuracy, scalability, rich multi-hop query capability, and seamless data integration. He demonstrates how vector RAG fails on numerical reasoning (e.g., Apple revenue query) while KAG returns precise evidence-based answers. Lam recommends a hybrid graph extraction combining LLM extraction with expert pruning, and reports benchmark results of 91% accuracy, 85% flexibility, and strong traceability and scalability.
