Knowledge graphs have significantly changed data accessibility with the rise of Large Language Models (LLMs). Retrieval-augmented LLM applications retrieve additional information from various sources to generate better and more accurate results. Vector similarity search is a strong bias in these applications, but structured information also has an important role to play in LLMs. Knowledge graphs can support LLM applications where users are interested in answering questions requiring highly-connected information, such as finding the shortest paths between data points or understanding complex biomedical relationships. They can also analyze supply chain scenarios and provide real-time insights into employee behavior and skills. The combination of structured and unstructured data retrieval paves the way for more accurate, reliable, and impactful results, extending beyond natural language answers into the realm of visually represented information.