Beginner's Guide to Graph RAG with Neo4j

Large language models offer incredible capabilities, but their outputs can sometimes lack accuracy or specificity. This course addresses this challenge by introducing you to Retrieval-Augmented Generation (RAG) using graph databases. Upon completing this course, you will be able to design and implement robust RAG systems that integrate knowledge graphs from Neo4j, significantly improving the quality and factual grounding of your AI chatbot outputs and other LLM-powered applications. What you'll learn: * Understand the fundamental principles of Retrieval-Augmented Generation (RAG). * Learn how knowledge graphs enhance the accuracy and relevance of LLM responses. * Build basic graph data models in Neo4j for contextual information storage. * Apply methods for querying graph data to retrieve relevant context for LLMs. * Practice integrating Neo4j-based RAG patterns into foundational LLM applications. * Explore modern strategies for optimizing RAG system performance and efficiency. * Configure basic prompt engineering techniques to effectively utilize retrieved context. The course begins with an introduction to RAG and the role of knowledge graphs, then guides you through setting up and querying Neo4j. You will then progress to practical exercises on integrating these components to build more reliable LLM applications. This course is designed for beginners interested in improving the reliability and accuracy of large language models, with no prior experience in graph databases or RAG systems required. Start your journey toward building more intelligent and trustworthy AI solutions today.