AI Enhancement with Knowledge Graphs - Mastering RAG Systems

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unleash the potential of AI systems by mastering Retrieval-Augmented Generation (RAG) techniques with Knowledge Graphs in this comprehensive course. You'll learn how to design, build, and query advanced Knowledge Graphs while integrating them with AI systems to boost contextual understanding and improve retrieval efficiency. The course begins with a solid introduction to Knowledge Graphs, including their structure, construction, and applications. You'll set up your development environment, dive into practical Neo4j implementations, and programmatically generate Knowledge Graphs. Through guided exercises, you'll extract real-world data, transform it into graph structures, and visually explore their interconnections. Moving further, you'll explore the synergy between Knowledge Graphs and RAG systems, creating vector indexes, embeddings, and integrating them into databases. Learn advanced querying methods, visualizations, and workflows for AI-powered use cases. By the end, you'll build a RAG-powered Knowledge Graph project, combining Neo4j and LangChain, to showcase the full flow of data transformation, retrieval, and application. This course is perfect for AI enthusiasts, data engineers, and developers eager to enhance their AI models with Knowledge Graphs. Prior experience with Python and basic AI concepts is recommended. Whether you’re at an intermediate or advanced level, you'll gain valuable, industry-relevant skills.