The second text description was generated with GPT-3.5-turbo as of 6/11/2024 (referred to as GPT hereafter), using multiple promptengineering techniques to reduce inaccuracies. We set the temperature to 0.3 and formatted the communication in a chat style. We first established the overall GPT behavior, providing the context and grounding by instructing it to act as a crystallography agent and give only scientifically correct facts (note that this of course does not guarantee accuracy, but it has been empirically shown that such prompting affects model performance ). We provided a CIF and instructed GPT to generate a text description by following a hidden chain of thought. The chain of thought included deducing the crystal system from the space group and lattice parameters, the number of atomic units from the formula, and linking Wyckoff positions with site coordinates. This procedure is based on empirical evidence that chain-of-thought prompting improves model accuracy . We also utilized few-shot learning, feeding two examples of input and output to the model. It does not contain full structure parameters but only some and also general structure features. There were no other requests for variability used in prompting.