Imagine a world where your telecom provider not only understands your needs but anticipates them, where network outages are a distant memory, and customer service is personalized, efficient, and always available. That's the promise of generative AI (GenAI), a technology poised to revolutionize the telecommunications industry. GenAI, particularly large language models (LLMs), are powerful tools capable of driving innovation across various sectors, and telecom is no exception. These models can analyze massive datasets, understand and respond to natural language, and even generate creative content. But how exactly is GenAI being applied in telecom? One of the most impactful applications is in customer support. LLM-powered chatbots can handle customer queries, troubleshoot technical issues, and provide personalized recommendations with remarkable speed and accuracy. This not only improves customer satisfaction but also frees up human agents to focus on more complex tasks. Another exciting area is network management and optimization. GenAI can analyze network logs, predict potential outages, and even suggest optimal configurations. This proactive approach can significantly reduce downtime and improve network performance. Field technicians are also benefiting from GenAI. LLM-powered virtual assistants can provide real-time guidance and support, helping technicians diagnose and resolve issues more efficiently. A fascinating example of GenAI in telecom is the development of standards-specific chatbots. These chatbots can quickly answer complex technical questions related to industry standards, making highly specialized information accessible to a broader audience. One key technique driving these advancements is Retrieval Augmented Generation (RAG). RAG allows LLMs to access and process external data sources, making their responses more accurate and reliable. A prime example is the O-RAN chatbot, which uses RAG to connect to technical specifications and answer intricate questions related to O-RAN standards. While the potential of GenAI in telecom is vast, challenges remain. Building effective telecom-specific LLMs requires access to large, domain-specific datasets. Furthermore, multi-modal GenAI models, which can process different data types such as radio signals and environmental data, need further development. The ongoing standardization efforts by bodies like 3GPP and O-RAN Alliance are crucial for ensuring interoperability and compatibility between different GenAI models. As these challenges are addressed, we can expect to see even more innovative applications of GenAI in telecom, paving the way for a more connected and intelligent future.