{"id":4447,"date":"2026-03-09T10:40:09","date_gmt":"2026-03-09T05:10:09","guid":{"rendered":"https:\/\/journal.ipem.edu.in\/computer_applications\/5g-enabled-smart-traffic-lights-performance-analysis-and-real-world-implementation-challenges-2\/"},"modified":"2026-03-09T12:03:10","modified_gmt":"2026-03-09T06:33:10","slug":"prompt-engineering-in-generative-ai-a-comparative-study-and-industry-analysis","status":"publish","type":"page","link":"https:\/\/journal.ipem.edu.in\/computer_applications\/prompt-engineering-in-generative-ai-a-comparative-study-and-industry-analysis\/","title":{"rendered":"Prompt Engineering in Generative AI: A Comparative Study and Industry Analysis"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"4447\" class=\"elementor elementor-4447\">\n\t\t\t\t\t\t<div class=\"elementor-inner\">\n\t\t\t\t<div class=\"elementor-section-wrap\">\n\t\t\t\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-3b3d6bf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3b3d6bf\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d724340\" data-id=\"d724340\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c722ad7 elementor-widget elementor-widget-heading\" data-id=\"c722ad7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Prompt Engineering in Generative AI: A Comparative Study and Industry Analysis<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-12c9a41 elementor-widget elementor-widget-html\" data-id=\"12c9a41\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t  <a class=\"author\" href=\"https:\/\/journal.ipem.edu.in\/computer_applications\/prompt-engineering-in-generative-ai-a-comparative-study-and-industry-analysis\/#author\"> <em> Ankur Narwal<\/em><\/a>,\r\n   <a href=\"https:\/\/journal.ipem.edu.in\/computer_applications\/prompt-engineering-in-generative-ai-a-comparative-study-and-industry-analysis\/#author\"> <em>Alka Kumari <\/em><\/a>\r\n\r\n\r\n\r\n\r\n\r\n                                        <p class=\"Issue\"><a href=\"https:\/\/journal.ipem.edu.in\/computer_applications\/prompt-engineering-in-generative-ai-a-comparative-study-and-industry-analysis\/\">Vol 10 ,  Issue 1 , December 2025<\/a><span class=\"pipe\"> | <\/span>Pages: 93-101\r\n                                        <\/p>\r\n                                        \r\n                    <p class=\"doi\">DOI: <a href=\"https:\/\/journal.ipem.edu.in\/computer_applications\/prompt-engineering-in-generative-ai-a-comparative-study-and-industry-analysis\/\"><\/a><\/p>\r\n\r\n                                        <p class=\"Published\"><span class=\"articledate\">Published Online: December, 2025<\/span><\/p>\r\n                                <p class=\"Download\">        <a href=\"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-content\/uploads\/2026\/03\/Article-09.pdf\" target=\"_blank\">Download Article<\/a><\/p>\r\n                    \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1eb397b elementor-widget elementor-widget-eael-adv-tabs\" data-id=\"1eb397b\" data-element_type=\"widget\" data-widget_type=\"eael-adv-tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t        <div data-scroll-on-click=\"no\" data-scroll-speed=\"300\" id=\"eael-advance-tabs-1eb397b\" class=\"eael-advance-tabs eael-tabs-horizontal eael-tab-auto-active  active-caret-on\" data-tabid=\"1eb397b\">\n            <div class=\"eael-tabs-nav\">\n                <ul class=\"\" role=\"tablist\">\n                                            <li id=\"author\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"true\" data-tab=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"author-tab\" aria-expanded=\"false\">\n                            \n                            \n                            \n                                                            <span class=\"eael-tab-title title-after-icon\" >Author Affiliations<\/span>                                                    <\/li>\n                                            <li id=\"abstract\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"abstract-tab\" aria-expanded=\"false\">\n                            \n                            \n                            \n                                                            <span class=\"eael-tab-title title-after-icon\" >Abstract<\/span>                                                    <\/li>\n                                            <li id=\"references\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"references-tab\" aria-expanded=\"false\">\n                            \n                            \n                            \n                                                            <span class=\"eael-tab-title title-after-icon\" >References<\/span>                                                    <\/li>\n                                            <li id=\"citation\" class=\"inactive eael-tab-item-trigger eael-tab-nav-item\" aria-selected=\"false\" data-tab=\"4\" role=\"tab\" tabindex=\"-1\" aria-controls=\"citation-tab\" aria-expanded=\"false\">\n                            \n                            \n                            \n                                                            <span class=\"eael-tab-title title-after-icon\" >Citation<\/span>                                                    <\/li>\n                                    <\/ul>\n            <\/div>\n            \n            <div class=\"eael-tabs-content\">\n\t\t        \n                    <div id=\"author-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"author-tab\">\n\t\t\t\t        <p class=\"Author-bg\"><b>Author Details<\/b><\/p><div class=\"card-body table-responsive\"><div class=\"autinfo\"><b class=\"sn\">1. <\/b>Ankur Narwal,, Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun -248007, India<\/div><div class=\"autinfo\"><b class=\"sn\">2. <\/b>Alka Kumari, Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun -248007, India<\/div><\/div>                    <\/div>\n\t\t        \n                    <div id=\"abstract-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"abstract-tab\">\n\t\t\t\t        <p>In this research, we examine prompt engineering in generative AI from two primary perspectives. First, we examine the performance of several prompting techniques, such as Zero-shot, Few-shot, and Chain-of-Thought. We then explore the practical applications of these strategies in real-world settings. To determine the best tools and timing, we evaluate models such as GPT-4, Deep Seek, and Gemini on tasks including coding, problem-solving, and content summarization. We rely on metrics such as accuracy, token efficiency, and output consistency to measure their effectiveness.We also look at how companies in industries like customer service and education use prompt engineering to improve chatbots and AI tutoring. The results show where each method works well and falls short, pointing to the most useful ways they can be applied.<\/p><p class=\"keywords\">Keywords<\/p><p>Gen AI, Prompting Techniques, Open AI, chatbot<\/p>                    <\/div>\n\t\t        \n                    <div id=\"references-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"references-tab\">\n\t\t\t\t        <p>[1]\u00a0\u00a0\u00a0\u00a0\u00a0 X. Amatriain, \u201cPrompt Design and Engineering: Introduction and Advanced Methods,\u201d May 2024, [Online]. Available: http:\/\/arxiv.org\/abs\/2401.14423<\/p><p>[2]\u00a0\u00a0\u00a0\u00a0\u00a0 S. Bubeck <em>et al.<\/em>, \u201cSparks of Artificial General Intelligence: Early experiments with GPT-4,\u201d Apr. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2303.12712<\/p><p>[3]\u00a0\u00a0\u00a0\u00a0\u00a0 L. Reynolds and K. McDonell, \u201cPrompt Programming for Large Language Models: Beyond the Few-Shot Paradigm,\u201d Feb. 2021, [Online]. Available: http:\/\/arxiv.org\/abs\/2102.07350<\/p><p>[4]\u00a0\u00a0\u00a0\u00a0\u00a0 M. Desmond and M. Brachman, \u201cExploring Prompt Engineering Practices in the Enterprise,\u201d Mar. 2024, [Online]. Available: http:\/\/arxiv.org\/abs\/2403.08950<\/p><p>[5]\u00a0\u00a0\u00a0\u00a0\u00a0 T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, \u201cLarge Language Models are Zero-Shot Reasoners,\u201d Jan. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2205.11916<\/p><p>[6]\u00a0\u00a0\u00a0\u00a0\u00a0 J. Wei <em>et al.<\/em>, \u201cChain-of-Thought Prompting Elicits Reasoning in Large Language Models,\u201d Jan. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2201.11903<\/p><p>[7]\u00a0\u00a0\u00a0\u00a0\u00a0 P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, \u201cPre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,\u201d <em>ACM ComputSurv<\/em>, vol. 55, no. 9, Sep. 2023, doi: 10.1145\/3560815.<\/p><p>[8]\u00a0\u00a0\u00a0\u00a0\u00a0 P. Sahoo, A. K. Singh, S. Saha, V. Jain, S. Mondal, and A. Chadha, \u201cA Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications,\u201d Mar. 2025, [Online]. Available: http:\/\/arxiv.org\/abs\/2402.07927<\/p><p>[9]\u00a0\u00a0\u00a0\u00a0\u00a0 K. Singhal <em>et al.<\/em>, \u201cLarge language models encode clinical knowledge,\u201d <em>Nature<\/em>, vol. 620, no. 7972, pp. 172\u2013180, Aug. 2023, doi: 10.1038\/s41586-023-06291-2.<\/p><p>[10]\u00a0\u00a0\u00a0\u00a0 T. B. Brown <em>et al.<\/em>, \u201cLanguage Models are Few-Shot Learners,\u201d Jul. 2020, [Online]. Available: http:\/\/arxiv.org\/abs\/2005.14165<\/p><p>[11]\u00a0\u00a0\u00a0\u00a0 H. Touvron<em>et al.<\/em>, \u201cLlama 2: Open Foundation and Fine-Tuned Chat Models,\u201d Jul. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2307.09288<\/p><p>[12]\u00a0\u00a0\u00a0\u00a0 P. Liang <em>et al.<\/em>, \u201cHolistic Evaluation of Language Models,\u201d Oct. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2211.09110<\/p><p>[13]\u00a0\u00a0\u00a0\u00a0 Y. Zhou <em>et al.<\/em>, \u201cLarge Language Models Are Human-Level Prompt Engineers,\u201d Mar. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2211.01910<\/p><p>[14]\u00a0\u00a0\u00a0\u00a0 Q. Lyu <em>et al.<\/em>, \u201cFaithful Chain-of-Thought Reasoning,\u201d Sep. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2301.13379<\/p><p>[15]\u00a0\u00a0\u00a0\u00a0 J. Kang, W. Xu, and A. Ritter, \u201cDistill or Annotate? Cost-Efficient Fine-Tuning of Compact Models,\u201d Jul. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2305.01645<\/p><p>[16]\u00a0\u00a0\u00a0\u00a0 C. Raffel <em>et al.<\/em>, \u201cExploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,\u201d Sep. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/1910.10683<\/p><p>[17]\u00a0\u00a0\u00a0\u00a0 Y. Bai <em>et al.<\/em>, \u201cConstitutional AI: Harmlessness from AI Feedback,\u201d Dec. 2022, [Online]. Available: http:\/\/arxiv.org\/abs\/2212.08073<\/p><p>[18]\u00a0\u00a0\u00a0\u00a0 K. Cobbe <em>et al.<\/em>, \u201cTraining Verifiers to Solve Math Word Problems,\u201d Nov. 2021, [Online]. Available: http:\/\/arxiv.org\/abs\/2110.14168<\/p><p>[19]\u00a0\u00a0\u00a0\u00a0 M. Chen <em>et al.<\/em>, \u201cEvaluating Large Language Models Trained on Code,\u201d Jul. 2021, [Online]. Available: http:\/\/arxiv.org\/abs\/2107.03374<\/p><p>[20]\u00a0\u00a0\u00a0\u00a0 J. W. Rae <em>et al.<\/em>, \u201cScaling Language Models: Methods, Analysis &amp; Insights from Training Gopher,\u201d Jan. 2022, [Online]. Available: http:\/\/arxiv.org\/abs\/2112.11446<\/p><p>[21]\u00a0\u00a0\u00a0\u00a0 OpenAI <em>et al.<\/em>, \u201cGPT-4 Technical Report,\u201d Mar. 2024, [Online]. Available: http:\/\/arxiv.org\/abs\/2303.08774<\/p><p>[22]\u00a0\u00a0\u00a0\u00a0 C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang, \u201cIs ChatGPT a General-Purpose Natural Language Processing Task Solver?\u201d Nov. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2302.06476<\/p><p>[23]\u00a0\u00a0\u00a0\u00a0 A. Bolotin, \u201cThe paradox of classical reasoning,\u201d Jul. 2022, doi: 10.1007\/s10701-022-00604-7.<\/p><p>[24]\u00a0\u00a0\u00a0\u00a0 V. Sanh <em>et al.<\/em>, \u201cMultitask Prompted Training Enables Zero-Shot Task Generalization,\u201d Mar. 2022, [Online]. Available: http:\/\/arxiv.org\/abs\/2110.08207<\/p><p>[25]\u00a0\u00a0\u00a0\u00a0 D. Zhou <em>et al.<\/em>, \u201cLeast-to-Most Prompting Enables Complex Reasoning in Large Language Models,\u201d Apr. 2023, [Online]. Available: http:\/\/arxiv.org\/abs\/2205.10625<\/p><p>[26]\u00a0\u00a0\u00a0\u00a0 A. Tamkin, M. Brundage, J. Clark, and D. Ganguli, \u201cUnderstanding the Capabilities, Limitations, and Societal Impact of Large Language Models,\u201d Feb. 2021, [Online]. Available: http:\/\/arxiv.org\/abs\/2102.02503<\/p>                    <\/div>\n\t\t        \n                    <div id=\"citation-tab\" class=\"clearfix eael-tab-content-item inactive\" data-title-link=\"citation-tab\">\n\t\t\t\t        <p>A. Narwal and A. Kumari, \u201cPrompt engineering in generative AI: A comparative study and industry analysis,\u201d <em>IPEM Journal of Computer Application &amp; Research<\/em>, vol. 10, pp. 93\u2013101, Dec. 2025.<\/p>                    <\/div>\n\t\t                    <\/div>\n        <\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Prompt Engineering in Generative AI: A Comparative Study and Industry Analysis Ankur Narwal, Alka Kumari Vol 10 , Issue 1 [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":""},"class_list":["post-4447","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/pages\/4447","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/comments?post=4447"}],"version-history":[{"count":10,"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/pages\/4447\/revisions"}],"predecessor-version":[{"id":4569,"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/pages\/4447\/revisions\/4569"}],"wp:attachment":[{"href":"https:\/\/journal.ipem.edu.in\/computer_applications\/wp-json\/wp\/v2\/media?parent=4447"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}