We look in more detail at the Text2Struc LLM performance depending on the prompt's level of detail. As described in the Methods, we used four types of prompts, i.e., structure text descriptions that differ in the level of detail included. Thus, (1) "String structure" explicitly lists all lattice parameters and sites needed for the code, (2) "GPT" mentions parameters only partially, which requires extrapolation on the part of the LLM to populate the Pymatgen code or CIF string, (3) "Robocrystal" contains the formula and space group as well as environmental descriptions with extra parameters that cannot be directly used in the code, and (4) "Spacegroup" includes only the formula and space group. Here, we compare the original structure to "API, code" generated structures, focusing on the most successful structure generation approach.