Classification Algorithm for Creating Optimal Questionnaires in Life Sciences and for AI

The number of user-to-machine interactions has increased dramatically over the last decade. With the introduction of fully functional generative AI, such as ChatGPT, Copilot, and Gemini, the number of the interactions and the associated amount of information will further increase in the nearest future. This work suggests an algorithm for optimizing the number of question-reply pairs between users and a machine. The goal of the optimization was to find the optimal amount of information for API and its server. As a result of the optimization, the cost of running the services for service providers and for users can be lowered. Furthermore, in this study, the optimization led to 23% increase of customer retention rate, 15% increase of revenue per customer, 17% drop of the customer acquisition cost, and 35% increase of customer engagement with AI.