The difference with higher and more consistent word counts per generative AI answer is likely due to Prompt 3 having less restrictive wording than Prompt 2, and only mentioning the correct molecular formula C 15 H 22 O 2 once. It is also likely that LMMs and LLMs generating compound predictions were less familiar with inputs containing 2D NMR textual data. The chain of 13 total word count of 2930 (Prompt 2) and 2924 (Prompt 3) may also be a factor regarding effective processing throughout the entire length of prompts . Generation times were longer for answers from Prompt 3, which was expected due to longer generations shown in Figure . A reason why models did not use the correct molecular formula despite convincing explanations of input data was that their training data was likely less inclusive of 2D NMR spectra. These processing techniques imply that automated methods of reducing high dimensional and complex data were likely applied to limit the need for additional pre-processing steps by the author. Although LMM or LLM processing of many spectrum's data does not solve the curse of dimensionality observed with traditional compute methods, further attempts to solve very large problems using these models in an automated or assistive fashion is likely worthwhile.