Performing other operations will require updating the dataset and additional LLM fine-tuning. Second, the structures used in the dataset do not represent the full complexity of existing materials. We argue that for teaching LLMs to write Pymatgen New design rules can be proposed to further mitigate hallucinations while using LLMs in crystal structure generation tasks. One approach is hybrid text-based and crystallography-constrained loss calculations. Initially, the LLM is fine-tuned following the standard protocol with text-based loss calculations. Then, the fine-tuning continues with an updated loss using an agent to execute the generated code and calculate the difference between the generated and original structures. Another design rule is to implement an iterative process where LLM generations are refined through energy minimization calculations, at the cost of increased computational resources and time. A well-known method to combat hallucinations is Multimodal Input Integration: the LLM can accept both textual descriptions and partial structural data as input, but this would require more complex data generation methods, model architectures, and training procedures. Another way to identify hallucinations is to incorporate an explainable AI layer that provides reasoning for structural generations, again at the cost of increasing model complexity and potentially reducing performance. Additionally, one can explicitly encode chemical knowledge (e.g., electronegativity, atomic radii) into the LLM architecture.