dongu-vestnik04.ru Вестник Донецкого университета. Серия 04. Технические науки Vestnik of Donetsk University. Series 04. Technical Sciences 2663-4228 514 10.5281/zenodo.19201896 Articles Статьи Research Article THE IMPACT OF PROMPT ENGINEERING ON THE ACCURACY OF LARGE LANGUAGES MODELS RESPONSES ВЛИЯНИЕ ТЕХНИКИ PROMPT ENGINEERING НА ТОЧНОСТЬ ОТВЕТОВ БОЛЬШИХ ЯЗЫКОВЫХ МОДЕЛЕЙ https://orcid.org/0009-0002-7691-2973 Slipenko Maksim Konstantinovich Слипенко Максим Константинович maxim@slipenko.com 1 https://orcid.org/0000-0002-9066-6134 Rychka Olga Valentinovna Рычка Ольга Валентиновна olga_rychka@mail.ru 2 ООО «Базальт СПО» BaseALT LLC ФГБОУ ВО «Донецкий национальный технический университет» Donetsk National Technical University 1 NO1 (27.0) №1 (27.0) 141 150 15 07 2026 Copyright ©; 2026, Вестник Донецкого университета. Серия 04. Технические науки Copyright ©; 2026, Vestnik of Donetsk University. Series 04. Technical Sciences 2026 Вестник Донецкого университета. Серия 04. Технические науки Vestnik of Donetsk University. Series 04. Technical Sciences Эта статья распространяется на условиях лицензии Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) https://dongu-vestnik04.ru/index.php/vestnik04/article/view/514

The article addresses the relevant task of improving the response accuracy of large language models (LLMs) in chatbots. The problem is defined by the models’ sensitivity to prompt structure and the likelihood of generating incorrect responses. To optimize LLM performance without retraining, several Prompt Engineering techniques are applied, including zero-shot, one-shot, few-shot, role prompting, zero-CoT, Re2, and RaR. Experiments on the GSM8K, MMLU, and BigBench Hard datasets demonstrated domain-dependent accuracy improvements of up to 15% when using combined approaches. The study confirms the effectiveness of adaptive prompting in enhancing the quality of LLM responses.

В статье рассматривается актуальная задача повышения точности ответов больших языковых моделей (LLM) в чат-ботах. Проблема определяется чувствительностью LLM к структуре промптов и вероятностью появления некорректных ответов. Для оптимизации работы без дообучения применяются техники Prompt Engineering: zero-shot, one-shot, few-shot, role prompting, zero-CoT, Re2 и RaR. Эксперименты на датасетах GSM8K, MMLU и BigBench Hard показали доменно-зависимый прирост точности до 15% при комбинированных подходах. Сделан вывод об эффективности адаптивного промптинга для повышения качества ответов LLM.

 Prompt Engineering, large language models, response accuracy Prompt Engineering, большие языковые модели, точность ответов

[Полный текст статьи отсутствует в исходных данных. Необходимо добавить текст из PDF или другого источника.]

Список литературы 1. GPT-4 Technical Report / OpenAI, J. Achiam, S. Adler [et al.] // arXiv preprint – 2024. – arXiv 2303.08774. 2. Velasquez-Henao, Ju. D. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering / Ju. D. Velasquez-Henao, C. Ja. Franco-Cardona, L. Cadavid-Higuita // Dyna-Colombia. – 2023. – Vol. 90, No. 230. – P. 9-17. – DOI 10.15446/dyna.v90n230.111700. – EDN ICGTLM. 3. Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code / J. Shin [et al.] // arXiv preprint – 2025. – arXiv 2310.10508. 4. Barkley, L. Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models / L. Barkley, B. van der Merwe // arXiv preprint. – 2024. – arXiv 2410.19385. 5. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications / P. Sahoo [et al.] // arXiv preprint. – 2025. – arXiv 2402.07927. 6. Kulikov, V. A Brief Overview Of Few-Shot Prompting In the Large Language Models / V. Kulikov, R. Neychev // Conference of Open Innovations Association, FRUCT. – 2023. – No. 33. – P. 364-370. – EDN YTPAJU. 7. Lee, S. Schema Retrieval for Korean Geographic Knowledge Base Question Answering Using Few-Shot Prompting / S. Lee, K. Yu // ISPRS International Journal of Geo-Information. – 2024. – Vol. 13, No. 12. – P. 453. – DOI 10.3390/ijgi13120453. – EDN PJNINJ. 8. The Prompt Report: A Systematic Survey of Prompt Engineering Techniques / S. Schulhoff [et al.] // arXiv preprint. – 2025. – arXiv 2406.06608. 9. Aher, G. Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies / G. Aher [et al.] // arXiv preprint. – 2023. – arXiv 2208.10264. 10. Chain of Thought Utilization in Large Language Models and Application in Nephrology / J. Miao, Ch. Thongprayoon, S. Suppadungsuk [et al.] // Medicina (Kaunas, Lithuania). – 2024. – Vol. 60, No. 1. – P. 148. – DOI 10.3390/medicina60010148. – EDN OCJFSO. 11. Large Language Models are Zero-Shot Reasoners / T. Kojima [et al.] // arXiv preprint. – 2023. – arXiv 2205.11916. 12. Re-Reading Improves Reasoning in Large Language Models / X. Xu [et al.] // arXiv preprint. – 2024. – arXiv 2309.06275. 13. Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves / Y. Deng [et al.] // arXiv preprint. – 2024. – arXiv 2311.04205. 14. Training Verifiers to Solve Math Word Problems / K. Cobbe [et al.] // arXiv preprint. – 2021. – arXiv 2110.14168. 15. Measuring Massive Multitask Language Understanding / D. Hendrycks [et al.] // arXiv preprint. – 2021. – arXiv 2009.03300. 16. Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them / M. Suzgun [et al.] // arXiv preprint. – 2022. – arXiv 2210.09261. 17. LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods / H. Li, Q. Dong, J. Chen [et al.] // arXiv preprint. – 2024. – arXiv 2412. 05579. 18. EvaluLLM: LLM assisted evaluation of generative outputs / M. Desmond, Z. Ashktorab, Q. Pan [et al.] // Companion Proceedings of the 29th International Conference on Intelligent User Interfaces. – ACM, 2024. – P. 30–32. – DOI 10.1145/3640544.3645216. 19. BLEU: a method for automatic evaluation of machine translation / K. Papineni, S. Roukos, T. Ward [et al.] // Proceedings of the 40th Annual Meeting on Association for Computational Linguistics – Association for Computational Linguistics, 2001. – P. 311. – DOI 10.3115/1073083.1073135. 20. A Survey on Evaluation Metrics for Machine Translation / S. Lee, J. Lee, H. Moon [et al.] // Mathematics. – 2023. – Т. 11, № 4. – P. 1006. – DOI 10.3390/math11041006. 21. LLM-as-a-qualitative-judge: automating error analysis in natural language generation / N. Chirkova, T. O. Ajayi, S. Aycock [et al.] // arXiv preprint. – 2025. – arXiv 2506.09147. 22. Тетеревенков, Д. Л. Экспертно-ориентированные методы оценки качества текстовой генерации больших языковых моделей / Д. Л. Тетеревенков // Мягкие измерения и вычисления. – 2025. – Т. 90, №5. – С. 30–37. – DOI 10.36871/2618-9976.2025.05.003. – EDN WRVOBU. REFERENCES LIST 1. GPT-4 Technical Report / OpenAI, J. Achiam, S. Adler [et al.] // arXiv preprint – 2024. – arXiv 2303.08774. 2. Velasquez-Henao, Ju. D. Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering / Ju. D. Velasquez-Henao, C. Ja. Franco-Cardona, L. Cadavid-Higuita // Dyna-Colombia. – 2023. – Vol. 90, No. 230. – P. 9-17. – DOI 10.15446/dyna.v90n230.111700. – EDN ICGTLM. 3. Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code / J. Shin [et al.] // arXiv preprint – 2025. – arXiv 2310.10508. 4. Barkley, L. Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models / L. Barkley, B. van der Merwe // arXiv preprint. – 2024. – arXiv 2410.19385. 5. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications / P. Sahoo [et al.] // arXiv preprint. – 2025. – arXiv 2402.07927. 6. Kulikov, V. A Brief Overview Of Few-Shot Prompting In the Large Language Models / V. Kulikov, R. Neychev // Conference of Open Innovations Association, FRUCT. – 2023. – No. 33. – P. 364-370. – EDN YTPAJU. 7. Lee, S. Schema Retrieval for Korean Geographic Knowledge Base Question Answering Using Few-Shot Prompting / S. Lee, K. Yu // ISPRS International Journal of Geo-Information. – 2024. – Vol. 13, No. 12. – P. 453. – DOI 10.3390/ijgi13120453. – EDN PJNINJ. 8. The Prompt Report: A Systematic Survey of Prompt Engineering Techniques / S. Schulhoff [et al.] // arXiv preprint. – 2025. – arXiv 2406.06608. 9. Aher, G. Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies / G. Aher [et al.] // arXiv preprint. – 2023. – arXiv 2208.10264. 10. Chain of Thought Utilization in Large Language Models and Application in Nephrology / J. Miao, Ch. Thongprayoon, S. Suppadungsuk [et al.] // Medicina (Kaunas, Lithuania). – 2024. – Vol. 60, No. 1. – P. 148. – DOI 10.3390/medicina60010148. – EDN OCJFSO. 11. Large Language Models are Zero-Shot Reasoners / T. Kojima [et al.] // arXiv preprint. – 2023. – arXiv 2205.11916. 12. Re-Reading Improves Reasoning in Large Language Models / X. Xu [et al.] // arXiv preprint. – 2024. – arXiv 2309.06275. 13. Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves / Y. Deng [et al.] // arXiv preprint. – 2024. – arXiv 2311.04205. 14. Training Verifiers to Solve Math Word Problems / K. Cobbe [et al.] // arXiv preprint. – 2021. – arXiv 2110.14168. 15. Measuring Massive Multitask Language Understanding / D. Hendrycks [et al.] // arXiv preprint. – 2021. – arXiv 2009.03300. 16. Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them / M. Suzgun [et al.] // arXiv preprint. – 2022. – arXiv 2210.09261. 17. LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods / H. Li, Q. Dong, J. Chen [et al.] // arXiv preprint. – 2024. – arXiv 2412. 05579. 18. EvaluLLM: LLM assisted evaluation of generative outputs / M. Desmond, Z. Ashktorab, Q. Pan [et al.] // Companion Proceedings of the 29th International Conference on Intelligent User Interfaces. – ACM, 2024. – P. 30–32. – DOI 10.1145/3640544.3645216. 19. BLEU: a method for automatic evaluation of machine translation / K. Papineni, S. Roukos, T. Ward [et al.] // Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. – Association for Computational Linguistics, 2001. – P. 311. – DOI 10.3115/1073083.1073135. 20. A Survey on Evaluation Metrics for Machine Translation / S. Lee, J. Lee, H. Moon [et al.] // Mathematics. – 2023. – Т. 11, № 4. – P. 1006. – DOI 10.3390/math11041006. 21. LLM-as-a-qualitative-judge: automating error analysis in natural language generation / N. Chirkova, T. O. Ajayi, S. Aycock [et al.] // arXiv preprint. – 2025. – arXiv 2506.09147. 22. Teterevenkov, D. L. Ekspertno-orientirovannye metody otsenki kachestva tekstovoi generatsii bolshikh iazykovykh modelei / D. L. Teterevenkov // Miagkie izmereniia i vychisleniia. – 2025. – T. 90, №5. – S. 30–37. – DOI 10.36871/2618-9976.2025.05.003. – EDN WRVOBU.