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Wisdom-Driven Knowledge Augmented Generation at Scale - Chin Keong Lam, Patho AI
Takeaway
For expert-advisor AI systems, model 'wisdom' as a feedback loop over knowledge/experience/insight/situation in a knowledge graph rather than relying on flat RAG retrieval.
Summary
- Patho.AI (NSF SBIR funded, started for drug discovery) builds 'Knowledge Augmented Generation' (KAG) — knowledge-graph-driven AI advisors, not just RAG retrievers
- Wisdom graph nodes: wisdom (core) → decision making → situation, with feedback loops from knowledge, experience and insight into wisdom
- For a competitive-analysis client, wisdom = strategy generator, knowledge = market data, experience = past campaigns, insight = industry DBs, situation = current product performance
- Prototyped in n8n with AI Agent nodes orchestrating multiple model providers (OpenAI, Anthropic, on-prem) plus a centralized graph updated by specialist sub-agents (e.g. sentiment from social media)
- Plan to graduate from n8n to LangChain or lighter-weight stacks; emphasizes that final decision quality depends on graph taxonomy more than the LLM choice
knowledge-graphskagn8n
Original description
The main thesis of the video is that by using a Wisdom-Driven Knowledge Graph, we can significantly enhance the quantitative analysis capabilities of Knowledge-Augmented Generation (KAG) systems. This allows for the creation of smarter AI systems that can not only retrieve information but also understand, reason, and provide expert-level advice. The talk argues that this approach surpasses traditional Retrieval-Augmented Generation (RAG) systems, which primarily rely on unstructured vector search. 00:00 Introduction to Patho AI and KAG 01:09 Defining Knowledge and Knowledge Graphs 01:56 KAG vs. RAG 02:37 The Wisdom-Decision Making-Situation Diagram 06:26 Practical Application: Competitive Analysis Chatbot 08:37 Implementation with N8n and Multi-Agent System 11:37 Why Use Knowledge Graphs over RAG 14:01 Challenges with Vector RAG and Numerical Reasoning 15:34 Building KAG Systems and Hybrid Models 16:45 Graph Extraction and Benchmarks 17:42 Conclusion and Resources Recorded at the AI Engineer World's Fair in San Francisco. Stay up to date on our upcoming events and content by joining our newsletter here: https://www.ai.engineer/newsletter