The action graphs that are generated by the fine-tuned LLM models represent a very simple, structured markup language. They are very generic in nature, and not tailored or restricted to specific hardware interfaces, APIs, or programming languages. However, due to the structured nature of the output with a clearly defined vocabulary and syntactic rules, these action graphs can be readily converted into other formats, such as XDL or -as demonstrated here -a node graph, by using standard rule-based text parsing, similar to what a compiler would do. These can then be further translated into executable code (e.g., python scripts) that can be run on the specific hardware of a selfdriving lab, as demonstrated below.