Knowledge Graphs for RAG: Building Context-Rich AI Applications

Standard retrieval-augmented generation (RAG) often struggles with complex, interconnected data, leading to incomplete or inaccurate AI responses. By combining vector search with knowledge graphs, you can provide large language models with the precise, structured context they need to deliver highly accurate answers. In this text-based course, you will learn how to design, build, and query knowledge graphs to supercharge your RAG applications. You will transition from basic keyword and vector searches to advanced hybrid retrieval methods that map real-world relationships. What you'll learn: - Understand the foundational concepts of graph databases, semantic relationships, and vector search integration. - Map unstructured text data into structured nodes and relationships using modern entity extraction techniques. - Write Cypher queries to retrieve connected data efficiently from a Neo4j database. - Implement hybrid search patterns that combine semantic vector search with structured graph traversal. - Design graph schemas tailored for optimal LLM context retrieval and reduced hallucination. - Apply best practices for maintaining and updating your knowledge graph as your data evolves. The course begins with essential terminology and the foundational mechanics of graph databases. You will then progress through practical, written examples that demonstrate how to construct schemas, write Cypher queries, and connect your knowledge graph to a RAG pipeline. This course is designed for software developers, data practitioners, and AI enthusiasts who are new to graph databases but want to build more reliable AI applications. No prior experience with Neo4j or graph theory is required. Start reading today to unlock the power of structured context for your AI systems.