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Modelling and Data Analysis
2025. Vol. 15, no. 4, 7–26
doi:10.17759/mda.2025150401
ISSN: 2219-3758 / 2311-9454 (online)

Evolutionary algorithms to generate prompts and verify responses of intelligent assistants

 
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Abstract

Tools for working with intelligent assistants (in particular, with ChatGPT and DeepSeek) have been developed. A new mathematical framework, specialized algorithms, and software, which allow for validating the content of intelligent assistant responses and generating prompts based on annotations (brief descriptions of the responses to prompts), have been presented. These tools make it possible to replace a prompt engineer or, at least, automate his work. The solution in use is based on the evolutionary algorithms which generate a sequence of prompts organized according to a specific logical scheme and include a quasi-genetic algorithm with pseudo-crossover and pseudo-mutation operations, followed by analysis of the intelligent assistant's responses with the aid of multivariate statistical analysis methods. The search for an acceptable result, in which the intelligent assistant itself is actively involved, is an iterative process converging toward a given solution. The applied approach is justified and illustrated by its application to solving psychological problems. The article is intended for programmers and mathematicians working with the large language models.

General Information

Keywords: intelligent assistant, large language model, artificial intelligence, evolutionary algorithm, quasi-genetic algorithm, prompt engineering, metric multidimensional scaling, psychology

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2025150401

Received 01.12.2025

Revised 03.12.2025

Accepted

Published

For citation: Kuravsky, L.S., Odintsov, D.A., Mikhailovsky, M.A. (2025). Evolutionary algorithms to generate prompts and verify responses of intelligent assistants. Modelling and Data Analysis, 15(4), 7–26. (In Russ.). https://doi.org/10.17759/mda.2025150401

© Kuravsky L.S., Odintsov D.A., Mikhailovsky M.A., 2025

License: CC BY-NC 4.0

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Information About the Authors

Lev S. Kuravsky, Doctor of Engineering, professor, Dean of the Computer Science Faculty, Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-3375-8446, e-mail: l.s.kuravsky@gmail.com

Dmitrii A. Odintsov, student, Computer Science Faculty, Moscow State University of psychology and education, Moscow, Russian Federation, ORCID: https://orcid.org/0009-0008-7082-700X, e-mail: dmitriyodintsov101@gmail.com

Michael A. Mikhailovsky, Research Assistant, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-7399-2800, e-mail: muxa172002@yandex.ru

Contribution of the authors

All authors participated in the discussion of the results and approved the final text of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

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