Graph Neural Networks (GNNs) are powerful tools for understanding relationships within data structured as graphs. However, designing the right GNN architecture for a specific task is complex and often involves tedious trial and error. New research explores a clever way to make this design process much easier using Large Language Models (LLMs). The core idea is to equip LLMs with the knowledge they need to design GNNs effectively. The researchers created a system called "DesiGNN" that works in three steps. First, it analyzes the graph's structure, looking at things like how connected the nodes are and how information flows through the graph. Second, it taps into a database of existing GNN designs and their performance on various graphs. This database acts like a textbook for the LLM, allowing it to learn which designs work well for different types of graphs. Third, using this learned knowledge, the LLM suggests an initial GNN design and then refines it based on how well it performs on the specific graph being analyzed. The results are promising. DesiGNN can generate effective GNNs much faster than traditional methods, often suggesting high-performing architectures within seconds. It also shows that LLMs, when given the right knowledge, can become proficient GNN designers, learning from past experiences and adapting to new challenges. This research opens exciting new possibilities for using LLMs to automate the design of complex AI models, potentially making them more accessible and efficient.