Self-driving labs (SDLs) and materials acceleration platforms (MAPs) have received much attention in recent years. Automating the synthesis of new molecules and materials on autonomous robotic platforms that integrate artificial intelligence (AI) and machine learning (ML) tools in a closed loop process has great potential for accelerating or even completely changing the way chemical experiments will be conducted in the future. However, to make these techniques viable, appealing, and approachable for a wide variety of primarily synthetically trained chemists and material scientists, it is important to ensure that the entry barriers to using these new tools are as low as possible. Hence, an intuitive way for generating automated synthesis workflows on such hardware using suitable interfaces to the robotic platforms will be a key component for the broad application of MAPs and SDLs in the scientific community. Natural language is perhaps one of the most intuitive ways for humans to express their intents and interact with any given system. The recent rise and large success of AI-based large language models (LLMs) demonstrates this very well and paved the way for numerous applications that rely heavily on user interaction through natural language.