Imagine a world where AI models could consult with each other, learning and improving together. That's the exciting premise behind a new research paper that introduces "LOGIN," a framework where Large Language Models (LLMs) act as consultants to Graph Neural Networks (GNNs). GNNs are powerful tools for understanding relationships within data represented as graphs, like social networks or molecular structures. However, they sometimes struggle with complex or noisy data. This is where the LLMs step in. LOGIN identifies the nodes or data points that the GNN finds most challenging. It then crafts concise prompts, feeding these tricky nodes to the LLM for expert advice. The LLM, with its vast knowledge base, provides predictions and explanations. But the real magic happens in how the GNN uses this feedback. If the LLM is right, the GNN updates its understanding of the node based on the LLM's explanation. If the LLM is wrong, the GNN assumes the problem lies in the connections between nodes and refines its analysis accordingly. This clever approach allows the GNN to learn from both the successes and failures of the LLM. The results are impressive. Even simple GNNs, when guided by LLMs, can achieve performance comparable to highly complex GNN architectures. This research opens doors to a future where AI models collaborate, leveraging each other's strengths to tackle increasingly complex problems. While challenges remain, such as scaling this approach to massive datasets, the potential for smarter, more efficient AI is undeniable.