Building Knowledge Graphs with Neo4j, LangChain, and LLMs

Modern AI applications require structured context to provide accurate, interconnected insights without hallucination. This text-based course equips you with the foundational principles and practical skills needed to transform unstructured data into powerful knowledge graphs and query them using modern language models. You will progress from foundational graph database concepts to writing Cypher queries, orchestrating AI workflows with LangChain, and implementing Graph RAG patterns. What you will learn: Understand core knowledge graph concepts, graph data modeling, and terminology; Query and manipulate graph databases effectively using Cypher in Neo4j; Integrate large language models using LangChain to extract entities and relationships from text; Build robust Graph Retrieval-Augmented Generation (Graph RAG) workflows for enhanced question answering; Implement structured prompt engineering and validation for consistent graph schema extraction. The curriculum starts with essential definitions and data modeling theory before moving into step-by-step written tutorials and structured code walkthroughs. This course is designed for beginners, developers, and data practitioners seeking a practical introduction to graph-driven AI with no prior graph database experience required. Start reading today to unlock the power of interconnected AI systems.