Designing an Optimal Prompt for Generative AI to Perform Hypothetical Reasoning in Technical Judgments of Road Bridges

This study proposes a method to induce hypothetico-deductive reasoning in Generative AI via Prompt Engineering, with the aim of assisting technical decision-making in road bridge maintenance. Specifically, it optimizes prompts input to a multimodal Large Language Model through Simulated Annealing and Hoeffding-UCB, building upon human-authored prompts that incorporate domain-specific terminology. Employing prompts optimized for hypothetico-deductive reasoning with a given model enabled the reproduction of the thought processes of experienced engineers. This yielded responses semantically closer to those of engineers and logical reasoning scores that were 1.2 - 1.4 times greater than those obtained by experienced engineers. However, no improvement was observed in the extent of hallucination suppression. The prompts developed in this study to induce hypothetico-deductive reasoning are characterized by versatility: they can be readily employed by engineers in routine practice and incorporated into algorithms utilizing various LLMs.