Initial tests prior to the main experiments were based on developing effective two sentence prompt templates. If too much detail was provided in the first sentence, the quality of the response would tend to decrease. In addition, if the phrase "beyond what is mentioned in the papers" was not included for some questions, models were previously found to be limited in returning new recommendations . The second sentence was optimized based on the author's prior studies, with the main finding that file location and image context or document text selection were more consistently obtained across models. Experiments using prompts regarding images located within documents did not yield high accuracies, so image captures were used. Additional requests regarding page numbers, line numbers or word numbers have experienced less success in models . The results indicate that there was not a model able to perform both tasks of processing images or documents at the highest level, represented by the two GPT models being more limited for image uploads and 3.5 Sonnet limited with documents 8, 9, 10. Another limitation was that a score of 10 was awarded for detailed responses, although some generations were not fully studied for applicability. Only a portion of images and documents from literature could be represented in the study. Future work will focus on solving chemical synthesis and characterization problems with LMMs using insight from this study. This will be accomplished with artificial intelligence computational speed advantages combined with new literature.