As a proof of concept, we demonstrate the translation of hydrazone synthesis (case study 2) on Python-based SDLs. Methods such as heating, stirring and dosing were tailored to resemble the configuration of an ALR setup, with manual addition of reagents other than solvents. The second prompt was adapted using the Python method definitions with updated mapping instructions (Fig. ). The resulting Python script accurately mapped functions for all the steps in hydrazine synthesis, showcasing the transferability of the prompt engineering approach across different instruments and programming languages (full conversation in ESI † Table ). Beside direct execution with Python-based SDLs, the functions can also serve as a backend for scripting operations in domainspecific XML (ESI † Table , example code provided in GitLab repo). This permits the potential transcription of more complex procedures with fewer token usages, as Python function calls do not require hierarchical templates like XML. With more sophisticated SDLs, potentially designed for material synthesis, there is the prospect of fully autonomous pipelines bridging the gap between literature and product.