Building Retrieval-Augmented Systems with Knowledge Graphs

Standard large language models often struggle with factual accuracy and hallucination when dealing with private or highly specific data. Connecting these models to structured, real-world data sources is the key to building reliable AI applications. This text-based course guides you through the fundamentals of Retrieval-Augmented Generation (RAG) and shows you how to supercharge your systems using knowledge graphs. You will transition from understanding basic language model limitations to designing intelligent retrieval workflows that combine unstructured text search with structured relational data. What you'll learn: Understand the foundational principles of Retrieval-Augmented Generation (RAG) and its role in modern AI architectures; Explore how knowledge graphs represent complex relationships and bridge the gap between structured data and language models; Learn the mechanics of vector databases, semantic search, and document chunking strategies; Combine vector search with graph-based retrieval to implement advanced hybrid RAG patterns; Apply prompt engineering techniques to help language models synthesize retrieved information accurately; Address common challenges such as database indexing, context window limitations, and system evaluation. The curriculum begins with essential definitions and core concepts of information retrieval before guiding you through step-by-step written walkthroughs of graph modeling and integration strategies. You will read through practical logic flows and architectural patterns that you can immediately apply to your own development projects. This course is designed for software developers, data enthusiasts, and technical beginners eager to build smarter AI systems, with no prior experience in graph databases or RAG required. Start reading today to unlock the power of structured knowledge in your AI applications.