Prompts A1 and A2 helped to identify how LLMs use dimension reduction and combinatorial optimization by locating relevant text in papers, and then providing LLM forecasts regarding mAb production improvements. Prompt OH provided the fastest of three ChatGPT-4o time estimates regarding how long it would take a human to create a quality answer based on Prompt A1 that was also provided to GenAI models. Prompts B1 and B2 identify how LLMs use monte carlo simulations and markov chain models by locating relevant numerical data in papers and then providing a table on how LLMs will improve upon mAb continuous production. Prompts C1 and C2 identify how LLMs use reinforcement learning and bayesian optimization by locating relevant numerical data in papers and then generating a report on how LLMs will improve upon mAb continuous production. 01) Prompt A1 (Part A Single Paper) -"How can monoclonal antibody production in this article be advanced through the incorporation of Large Language Model integrated problem solving (dimension reduction, combinatorial optimization, etc.)? Identify the selections of text from the article in quotations that will see the greatest improvements in speed or performance."