Imagine an AI assistant managing the complex dance of electricity across a power grid. This isn't science fiction, it's getting closer to reality thanks to large language models (LLMs). Traditionally, LLMs, known for their text generation prowess, have struggled with the intricate world of power system simulations. The knowledge gap between AI and the specialized software used in power systems has been a major roadblock. However, groundbreaking research introduces a new framework that bridges this gap. Researchers have developed a modular system that allows LLMs to interact with and even control power system simulation tools they’ve never encountered before. This system goes beyond simple prompt engineering. It breaks down complex simulation requests into smaller, manageable tasks, allowing the LLM to learn and adapt. One key innovation is an enhanced retrieval system. This allows the LLM to access and process information from multiple sources, ensuring it has the knowledge needed to run the simulation. The framework also incorporates a feedback loop, allowing the LLM to learn from its mistakes and refine its approach. This iterative process leads to greater accuracy over time. Tested on a power system simulation toolbox called DALINE, this framework boosted the accuracy of GPT-4 from 0% to an impressive 96%. This jump demonstrates the potential of LLMs to not just analyze text, but to perform complex, real-world tasks in specialized fields. The implications are significant. This framework opens the door to AI-driven power grid management, potentially leading to increased efficiency, faster problem-solving, and more robust systems. While this research focused on a single simulation tool, future work aims to expand its capabilities to encompass a broader range of power system software. This advancement could revolutionize how we design, operate, and manage our power grids, ushering in a new era of intelligent energy systems.