Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Discover how to integrate Large Language Models (LLMs) with knowledge graphs in this comprehensive workshop presentation from the Toronto Machine Learning Series. Learn to overcome LLM limitations through Retrieval Augmented Generation (RAG) techniques while leveraging Neo4j's graph database capabilities for enhanced contextual responses. Follow along with the development of a personal messenger application that delivers personalized product recommendations using RAG patterns. Gain practical experience combining LLMs with Neo4j knowledge graphs and graph data science algorithms to uncover hidden patterns, derive new relationships, and create sophisticated GenAI applications suited for enterprise deployment. Led by Neo4j Senior Solutions Engineer Dr. Yizhi Yin, explore how graph database structures can provide the dynamic, interconnected foundation needed for more accurate and contextually aware AI systems.