Prompt IIf: Create a very large revised CHL-YN-IgG1 (B-08) .csv table for download based on the new attached summaries regarding how the original authors desired certain intracellular metabolites or intracellular metabolite relationships that could be improved using a LLM or other authors' methods. Provide columns with original numerical values followed by new forecasted values (not percent increases), identifying authors' names in another column, and indicating whether a specific breakthrough is expected with the use of the LLM or other authors' methods. ChatGPT-4o was not able to provide original author numbers in quotes as requested in Protocol 4 shown in Table , but had the most consistent quality for generating insights regarding Qlac, lactate shifts, and CHO cell growth. For Paper 02, the model projected "An LLM incorporating Bokelmann's model could further test and optimize these feeds dynamically, predicting higher Qlac values of around 1.2 mmol/gDW/h by balancing glycolytic and oxidative pathways." An example workflow is shown in Figure . ChatGPT-4o provided insight regarding LLM incorporation that could reduce Day 9 lactate consumption over glucose consumption shift "to around 7 days, as they predict carbon source transitions faster due to refined parameterization."