Large Language Models (LLMs) are powerful but notoriously ungrounded as they generate fluent, plausible text that isn’t always factual, consistent, or explainable. One promising way to address these issues is by connecting LLMs with Knowledge Graphs, namely structured, explicit representations of knowledge that can provide context, constraints, and verifiable facts. The main paradigm for doing this is Graph-based Retrieval-Augmented Generation (GraphRAG), which integrates graph-based reasoning and retrieval into the generation process. In this talk, I’ll introduce the core ideas behind GraphRAG, describe common design patterns, and outline the steps and tools needed to implement such systems in practice.