The recent advancements in the field of network management and optimization are significantly leveraging the capabilities of large language models (LLMs). These models are being integrated into various frameworks to automate and enhance the robustness and efficiency of network designs. One notable trend is the shift towards knowledge-free optimization techniques, where LLMs are employed to manage network tasks without prior system knowledge, demonstrating comparable performance to traditional methods. Additionally, there is a growing focus on developing tools that provide detailed insights into network traffic distribution, aiding in the identification and mitigation of performance bottlenecks. These tools are crucial for optimizing routing strategies and improving the performance of distributed AI workloads. Furthermore, innovative approaches like dedicated network slicing for LLMs are emerging to address the unique challenges posed by the increasing adoption of these models, enhancing response speed and resource efficiency in wireless networks. Overall, the field is moving towards more intelligent, adaptive, and efficient network management solutions driven by advanced AI technologies.