Imagine controlling robots with simple voice commands – a world where complex programming is replaced by intuitive instructions. Large Language Models (LLMs) like ChatGPT are making this dream a reality, translating our words into actions for robots. But there’s a catch: ensuring the safety of these actions is critical, especially as robots take on more responsibilities in our world. Recent research tackles this challenge head-on by introducing a 'safety layer' to the process. Before an LLM's generated code is sent to a robot (in this case, a simulated drone), it's scrutinized by a fine-tuned GPT-4 model. This model acts as a gatekeeper, identifying and blocking potentially unsafe actions. Imagine it as a robotic guardian angel, preventing crashes or other dangerous maneuvers. The researchers trained this safety layer using a clever combination of techniques. They created a dataset of safe and unsafe drone commands, fine-tuned the GPT-4 model to identify them, and boosted its ability to reason by integrating a 'knowledge graph' packed with drone safety regulations. This knowledge graph acts like a rulebook, guiding the model to make informed decisions about safe behaviors. The results are promising. This LLM-powered safety system demonstrates a significant improvement in identifying and preventing unsafe drone actions, bringing us closer to a world where robots operate seamlessly and safely alongside humans. While this research focused on drones, the core idea—using LLMs to verify the safety of robotic actions—has the potential to revolutionize how we interact with robots across various domains. Think of self-driving cars, robotic surgeons, or even household robots, all operating within a secure framework of verified actions, thanks to the watchful eye of an LLM safety net. However, the journey isn't over. Researchers are already looking ahead, refining these models to handle increasingly complex scenarios and regulations. The challenge lies in ensuring these systems can adapt to unpredictable real-world environments while maintaining a robust safety framework. But one thing is certain: LLMs are paving the way for a future where human-robot collaboration is safer, smarter, and more intuitive than ever before.