Imagine an AI that not only identifies objects in an image but also understands the relationships between them. This is the goal of Scene Graph Generation (SGG), a crucial task for computer vision that aims to create a structured understanding of images. Traditional SGG models often stumble when faced with novel objects or relationships outside their training data. Enter *Open-Vocabulary Scene Graph Generation (OV-SGG)*, a cutting-edge field tackling this limitation. Existing OV-SGG methods, while promising, have relied on fixed text descriptions of relationships, hindering their ability to capture the nuanced interactions within a scene. Researchers have now developed a groundbreaking framework called Relation-Aware Hierarchical Prompting (RAHP) to address this very problem. RAHP leverages the power of large language models (LLMs) in a clever way. Instead of using fixed prompts, RAHP generates *dynamic, hierarchical prompts* that capture both the broad relationship between objects (like "person sitting on chair") and the fine-grained details of the interaction (like "person's hips making contact with the chair seat"). This hierarchical approach dramatically expands the range of relationships the AI can understand. Imagine trying to describe the relationship between a person and a bicycle. A simple prompt like "person riding bicycle" is helpful, but it doesn't capture the specifics. RAHP delves deeper, generating prompts like "person gripping handlebars," "person's feet pedaling," and "bicycle wheels rotating." This level of detail allows the AI to build a much richer understanding of the scene. Furthermore, RAHP incorporates a "dynamic selection" mechanism. This acts like a filter, ensuring the AI focuses only on the most relevant prompts for a given image, eliminating noise and boosting accuracy. Tests on standard datasets like Visual Genome and Open Images v6 show RAHP significantly outperforms previous OV-SGG methods, particularly in identifying novel relationships. This breakthrough paves the way for AI systems with a deeper, more flexible understanding of the visual world. From self-driving cars navigating complex scenes to robots interacting seamlessly with their environment, RAHP unlocks exciting possibilities for the future of computer vision.