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Journal articles on the topic 'AI Prompt Engineering'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Patil, Rajvardhan, Thomas F. Heston, and Vijay Bhuse. "Prompt Engineering in Healthcare." Electronics 13, no. 15 (2024): 2961. http://dx.doi.org/10.3390/electronics13152961.

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The rapid advancements in artificial intelligence, particularly generative AI and large language models, have unlocked new possibilities for revolutionizing healthcare delivery. However, harnessing the full potential of these technologies requires effective prompt engineering—designing and optimizing input prompts to guide AI systems toward generating clinically relevant and accurate outputs. Despite the importance of prompt engineering, medical education has yet to fully incorporate comprehensive training on this critical skill, leading to a knowledge gap among medical clinicians. This articl

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Wu, Nan. "A Comparative Analysis of Human-Written vs. AI-Generated Prompts for Task Executions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–3. https://doi.org/10.55041/ijsrem39321.

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Abstract—This study examines the effectiveness of AI-generated prompts in task execution compared to human-written prompts. AI-generated prompts showed comparable performance to expert-crafted prompts and offered greater flexibility, making prompt engineering more accessible to users. Future work will explore more advanced prompt templates. Keywords—AI-generated prompts, prompt engineering, task execution, content quality, humanized writing

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Siegle, Del. "Using AI Prompt Engineering to Improve Gifted Students’ Questioning." Gifted Child Today 48, no. 1 (2024): 68–72. https://doi.org/10.1177/10762175241289886.

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As artificial intelligence (AI) becomes more integrated into education, the ability to craft precise prompts, or “prompt engineering,” takes on new importance. Gifted students, with their natural creativity, analytical skills, and ability to connect ideas across disciplines, are well-positioned to excel in prompt engineering. Through specific questioning and follow-up prompts, gifted learners can deepen their engagement with AI, improving the quality and relevance of AI-generated responses. The introduction of models like CRAFT and strategies such as prompt chaining and flipped interaction pat

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Shepherd, Jennifer, and Donald Geisheimer. "FAQs: AI and prompt engineering." American Nurse Journal 19, no. 6 (2024): 14–19. http://dx.doi.org/10.51256/anj062414.

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Prashant, Awasthi. "Effect of Prompt Engineering on Education Sector: A mixed case study." International Journal of Leading Research Publication 2, no. 10 (2021): 1–12. https://doi.org/10.5281/zenodo.15107514.

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The effect of prompt engineering on the education industry with a focus on enhancing teaching techniques, student engagement, and learning results. Personalized prompts are created with the assistance of AI-based tools like ChatGPT to facilitate various educational requirements, making adaptive learning processes possible. This research utilizes a mixed-methods case study method grounded in qualitative and quantitative data to examine the efficacy of prompt engineering in actual classrooms. Evidence has been found that well-designed prompts produce greater critical thinking, deeper discussions

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IŞIN, Zişan Cihangir, Hilal FIDAN, Beyşan Tarık IŞIN, Erşan IŞIN, and Tamer IŞIN. "Is Prompt Engineering a Profession?" International Journal of Artificial Intelligence & Applications 15, no. 3 (2024): 29–39. http://dx.doi.org/10.5121/ijaia.2024.15303.

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Prompt Engineering, the systematic design and construction of prompts for human-AI interaction, raises questions regarding its professional status. This paper examines Prompt Engineering and evaluates whether it qualifies as a distinct profession. Through an analysis of its defining characteristics, including specialized skills, ethical considerations, and societal impact, this study explores the parallels between Prompt Engineering and established professions. Drawing on examples from various fields, it argues for the recognition of Prompt Engineering as a legitimate profession. By addressing

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AIRCC. "IS PROMPT ENGINEERING A PROFESSION?" International Journal of Artificial Intelligence & Applications (IJAIA) 15, no. 3 (2024): 29–39. https://doi.org/10.5121/ijaia.2024.15303.

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Prompt Engineering, the systematic design and construction of prompts for human-AI interaction, raisesquestions regarding its professional status. This paper examines Prompt Engineering and evaluateswhether it qualifies as a distinct profession. Through an analysis of its defining characteristics, includingspecialized skills, ethical considerations, and societal impact, this study explores the parallels betweenPrompt Engineering and established professions. Drawing on examples from various fields, it argues forthe recognition of Prompt Engineering as a legitimate profession. By addressing the

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AIRCC. "IS PROMPT ENGINEERING A PROFESSION?" International Journal of Artificial Intelligence & Applications (IJAIA) 15, no. 3 (2024): 29–39. https://doi.org/10.5121/ijaia.2024.15303.

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Prompt Engineering, the systematic design and construction of prompts for human-AI interaction, raisesquestions regarding its professional status. This paper examines Prompt Engineering and evaluateswhether it qualifies as a distinct profession. Through an analysis of its defining characteristics, includingspecialized skills, ethical considerations, and societal impact, this study explores the parallels betweenPrompt Engineering and established professions. Drawing on examples from various fields, it argues forthe recognition of Prompt Engineering as a legitimate profession. By addressing the

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Wu, Nan. "A Comparative Analysis of Human-Written vs. AI-Generated Prompts for Task Executions." Journal of Artificial Intelligence & Cloud Computing 3, no. 6 (2024): 1–5. https://doi.org/10.47363/jaicc/2024(3)410.

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This study examines the effectiveness of AI-generated prompts in task execution compared to human-written prompts. AI-generated prompts showed comparable performance to expert-crafted prompts and offered greater flexibility, making prompt engineering more accessible to users. Future work will explore more advanced prompt templates.

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Panneer Selvam Viswanathan. "Prompt Engineering for Conversational AI Systems: A Systematic Review of Techniques and Applications." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 733–41. https://doi.org/10.32628/cseit25111276.

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This article comprehensively analyzes prompt engineering techniques in conversational AI systems, focusing on their implementation and impact on large language model (LLM) performance. The article examines the fundamental principles of effective prompt design, including clarity, contextual framing, and instructional phrasing, while exploring advanced techniques such as prompt chaining, few-shot learning, and domain-specific adaptations. The article investigates role-based prompting strategies and parameter optimization methods, addressing critical challenges in bias mitigation and response con

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Dr.A.Shaji, George, and George A.S.Hovan. "The Emergence of Prompt Engineering in India: Assessing the Potential for a New Generation of AI Talent." Partners Universal International Innovation Journal (PUIIJ) 01, no. 06 (2023): 1–18. https://doi.org/10.5281/zenodo.10125681.

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Prompt engineering is an emerging field focused on crafting effective prompts to elicit useful outputs from artificial intelligence systems. As AI grows more advanced, particularly in natural language processing, demand has arisen for specialized prompt engineers who can optimize human-AI interactions. This paper assesses India's potential to become a leader in prompt engineering by developing talent and establishing educational programs. Prompt engineering combines computer science, linguistics, psychology, and creativity. While present globally, demand for prompt engineering is estimated to

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Velásquez-Henao, Juan David, Carlos Jaime Franco-Cardona, and Lorena Cadavid-Higuita. "Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering." DYNA 90, no. 230 (2023): 9–17. http://dx.doi.org/10.15446/dyna.v90n230.111700.

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ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The appr

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Chirag, Amrutlal Pethad. "Guide to Implementing Prompt Engineering in Salesforce." Journal of Scientific and Engineering Research 10, no. 10 (2023): 176–89. https://doi.org/10.5281/zenodo.13918812.

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The document outlines a comprehensive guide on implementing prompt engineering in Salesforce, detailing steps for integrating AI services, creating custom Apex classes, testing and refining prompts, deploying and monitoring performance, and optimizing for continuous improvement. It emphasizes best practices such as understanding business context, clarity in prompts, iterative testing, and ethical considerations, while providing specific instructions for creating various types of prompt templates, including text completion, summarization, and classification.

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I. Nezer, Ahmed, Bashar AlEsawi, and Wisam Makki Salim. "Prompt Engineering: Unleashing the Power of Large Language Models to Defend Against Social Engineering Attacks." Iraqi Journal For Computer Science and Mathematics 5, no. 3 (2024): 404–16. http://dx.doi.org/10.52866/ijcsm.2024.05.03.024.

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Prompt Engineering is an emerging area of study that pertains to the act of conceptualizing, perfecting, and executing prompts that guide an AI model to an intended purpose. The AI model is an LLM, which they are the “hit” of our time and probably the controversial type of AI. They are capable of executing several tasks using natural language processing algorithms. Due to their ease of use and fast development, they are becoming highly dependent. We found that to interact correctly with these models and gain the best performance, several techniques should be taken into consideration. Moreover,

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Joshi. "Retraining US Workforce in the Age of Agentic Gen AI: Role of Prompt Engineering and Up-Skilling Initiatives." International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) 5, no. 1 (2025): 543–57. https://doi.org/10.5281/zenodo.14884966.

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This work reviews U.S. workforce retention, AI upskilling, prompt engineering, workforce development, and automation in the context of recent advancements in agentic generative AI. The rapid integration of artificial intelligence (AI) across industries has raised concerns over potential job displacement within the US workforce. However, targeted upskilling—particularly through training in prompt engineering, a key skill for interacting with large language models—offers promising avenues toempower employees and retain talent. This review paper synthesizes insight

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Ramona Markoska and Aleksandar Markoski. "Effective prompt engineering for generative AI in C++ programming tasks." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 1390–97. https://doi.org/10.30574/wjarr.2025.25.2.0516.

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The rise of Generative AI, propelled by Large Language Models (LLMs), has opened new opportunities to streamline programming tasks across various domains. In C++ programming, renowned for its intricate syntax, memory management complexities, and performance-critical applications, Generative AI offers invaluable support for code generation, optimization, and debugging. However, the effectiveness and accuracy of these AI models rely heavily on the application of prompt engineering—a technique that involves crafting precise, contextually relevant queries to guide the AI's response.This paper delv

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Priescu, Iustin, Geanina Silviana Banu, Tatiana Corina Dosescu, and Marius Irinel Banu. "Prompt Engineering in Cybersecurity – Achieving Technological Edge." Land Forces Academy Review 30, no. 2 (2025): 291–302. https://doi.org/10.2478/raft-2025-0028.

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Abstract Prompt Engineering has emerged as a critical technique for optimizing interactions with Large Language Models (LLMs), enhancing their precision and effectiveness in various applications. This paper explores the role of structured prompting techniques in cybersecurity, focusing on their potential to improve threat detection, security automation, and incident response. By leveraging Chain-of-Thought (CoT) and Multimodal CoT prompting, the study evaluates how well-crafted prompts enhance LLM capabilities in analyzing network traffic, identifying phishing attempts, automating security rep

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Bozkurt, Aras, and Ramesh C. Sharma. "Generative AI and Prompt Engineering: The Art of Whispering to Let the Genie Out of the Algorithmic World." Asian Journal of Distance Education 18, no. 2 (2023): i—vii. https://doi.org/10.5281/zenodo.8174941.

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The capabilities of generative AI in education, serving as a co-creator, highlight the crucial role of prompt engineering for optimal interactions between hum<strong>a</strong>n<strong>i</strong>ty and Large Language Models (LLMs) that utilize Natural Language Processing (NLP). Generative AI's potential lies in responding to well-crafted prompts, making them essential for unleashing its capabilities in generating authentic content. To optimize this process, prompt engineers, encompassing various stakeholders in education, must grasp how language nuances impact generative AI's responses

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Heston, Thomas F., and Charya Khun. "Prompt Engineering in Medical Education." International Medical Education 2, no. 3 (2023): 198–205. http://dx.doi.org/10.3390/ime2030019.

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Artificial intelligence-powered generative language models (GLMs), such as ChatGPT, Perplexity AI, and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the chal

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Darmawan, Andreas James, I. Made Gede Arimbawa, Anita Heptariza, and Hosea Brayen. "HARNESSING AI IMAGE GENERATOR PROMPT ENGINEERING FOR ACADEMIC EXCELLENCE." Proceeding Bali-Bhuwana Waskita: Global Art Creativity Conference 4 (December 29, 2024): 192–202. https://doi.org/10.31091/bbwp.v4i1.478.

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This study explores the role of prompt engineering in visual communication design education, specifically examining its application with AI image generators. With the rise of AI technologies, prompt engineering has become an essential skill for creative professionals to maximize the potential of AI tools in generating high-quality visuals. The purpose of this research is to assess the effectiveness of prompt engineering skills among visual communication design students and identify factors that contribute to successful prompt formulation. Research methods include case study analysis, surveys,

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Singh, Atulesh Pratap, Dr Ajay Singh, and Dr Pushpneel Verma. "Prompt Engineering in AI driven Indian Healthcare." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–9. http://dx.doi.org/10.55041/ijsrem36348.

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The phenomenal growth of artificial intelligence (AI) and machine learning (ML) in recent years has revolutionized various sectors, including healthcare. Particularly the large language models (LLMs) developed recently by various companies such as Google, Microsoft, Nvidia, OpenAI have demonstrated remarkable capabilities in understanding and generating contextualized text thus making them valuable tools that can be effectively utilized by healthcare industry and service providers. Prompt engineering plays a crucial role leveraging full potential of such LLMs. Prompt Engineering is the techniq

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Bakhtiyari, Dr Syed Valiullah. "Descriptive Case Analysis on the Application of Prompt Engineering in Business Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38060.

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In recent years, the application of AI has been witnessed in various domains and disciplines, mainly AI in terms of large-scale pre-trained language models (LMs) and Visual Language models which are the prompt engineering types that have gained increasing attention in the field of Business and Management field. The prompt engineering approach is considered vital leverage in the areas of BM tasks. In this AI-generated review, we will try to discuss various aspects of prompt engineering encompassing the domain of management and also highlight various applications in this area. This paper also tr

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Meynhardt, Charlotte, Patrick Meybohm, Peter Kranke, and Carlos Ramon Hölzing. "Advanced Prompt Engineering in Emergency Medicine and Anesthesia: Enhancing Simulation-Based e-Learning." Electronics 14, no. 5 (2025): 1028. https://doi.org/10.3390/electronics14051028.

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Medical education is rapidly evolving with the integration of artificial intelligence (AI), particularly through the application of generative AI to create dynamic learning environments. This paper examines the transformative role of prompt engineering in enhancing simulation-based learning in emergency medicine. By enabling the generation of realistic, context-specific clinical case scenarios, prompt engineering fosters critical thinking and decision-making skills among medical trainees. To guide systematic implementation, we introduce the PROMPT+ Framework, a structured methodology for desig

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Cotroneo, Peter, and James Hutson. "Generative AI tools in art education: Exploring prompt engineering and iterative processes for enhanced creativity." Metaverse 4, no. 1 (2023): 14. http://dx.doi.org/10.54517/m.v4i1.2164.

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<p>The rapid development and adoption of generative artificial intelligence (AI) tools in the art and design education landscape have introduced both opportunities and challenges. This timely study addresses the need to effectively integrate these tools into the classroom while considering ethical implications and the importance of prompt engineering. By examining the iterative process of refining original ideas through multiple iterations, verbal expansion, and the use of OpenAI’s DALL-E2 for generating diverse visual outcomes, researchers gain insights into the potential benefits and p

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Какун, Артем, and Сергій Титенко. "GENERATIVE AI AND PROMPT ENGINEERING IN EDUCATION." Modern engineering and innovative technologies, no. 29-01 (October 30, 2023): 117–21. http://dx.doi.org/10.30890/2567-5273.2023-29-01-052.

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The development of generative AIs and the variability of their use are still at the level of research and active development simultaneously. However, it has already become clear that the emergence of generative AI significantly impacts many industries, in

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SKVORTSOVA, Svitlana, Tetiana SYMONENKO, and Kira HNEZDILOVA. "PROMPT ENGINEERING AS A TOOL FOR OPTIMIZING THE PROFESSIONAL ACTIVITIES OF EDUCATORS: AN EXPERIMENTAL STUDY OF THE EFFECTIVENESS OF GENERATIVE LANGUAGE MODELS." Cherkasy University Bulletin: Pedagogical Sciences, no. 1 (2025): 20–33. https://doi.org/10.31651/2524-2660-2025-1-20-33.

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Introduction. Modern education is undergoing a transformation driven by digital technologies and the impact of artificial intelligence (AI) on the learning process. One of the most innovative directions is the application of generative language models and prompt engineering to optimize routine educational tasks. Given the prevalence of clip-thinking and information overload among Generation Alpha, traditional learning approaches require adaptation. Prompt engineering offers new opportunities for automating educational content creation, personalizing learning processes, and improving overall ef

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Coffin Murray, Meg. "Bridging the Generative AI Literacy Gap: A Guide to Introducing Prompt Engineering in University Courses." Issues in Informing Science and Information Technology 22 (2025): 010. https://doi.org/10.28945/5516.

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Aim/Purpose To address the gap in students’ effective use of generative AI tools, this paper presents a framework to introduce university students to the principles and practices of prompt engineering – the art and science of crafting precise and purposeful inputs to guide LLMs in generating accurate and useful outputs. This paper aims to equip students with strategies to interact meaningfully with AI chatbots for academic success. Background Generative AI tools, like ChatGPT, are widely adopted in educational settings, yet many students lack the skills to harness their full potential. This pa

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Vidhani, Dinesh V., and Manoharan Mariappan. "Optimizing Human–AI Collaboration in Chemistry: A Case Study on Enhancing Generative AI Responses through Prompt Engineering." Chemistry 6, no. 4 (2024): 723–37. http://dx.doi.org/10.3390/chemistry6040043.

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“Are we asking the right questions?” seems cliché, but for ChatGPT, it is a pivotal tool to ensure the accuracy of responses. While ChatGPT-3.5’s training on the vast database promises to revolutionize STEM education and research, this investigation shows the importance of precise communication and prompt engineering in guiding ChatGPT-3.5 toward reliable and accurate responses, particularly in chemistry. For instance, emphasizing context, clearly defining symbols, and focusing on field-specific instructions can dramatically improve its performance. Furthermore, avoiding open-ended prompts and

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Khatri, Savi. "Prompt-Layered Architecture: A New Stack for AI-First Product Design." International Journal of Scientific Research and Management (IJSRM) 12, no. 09 (2024): 1487–98. https://doi.org/10.18535/ijsrm/v12i09.ec09.

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With the advancement of large language models powering next-generation applications, there is an increasing demand for architectural frameworks that treat prompts as modular, orchestratable, and extendible parts of a software system. The traditional methods of AI integration have treated prompt engineering as some kind of ad hoc or application-specific task with no connection to systematic design principles or software architecture standards. The paper introduces the Prompt-Layered Architecture (PLA), a new architectural style where prompts have been elevated into first-class citizens of the s

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Jha, Aadya. "Prompt Optimization for AI-Powered Recipe Generation: Challenges and Insights." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 696–703. http://dx.doi.org/10.22214/ijraset.2024.65150.

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In today's digital age, artificial intelligence (AI) is transforming the culinary landscape by enabling personalized recipe generation. AI-powered recipe generation systems can offer tailored cooking solutions based on user preferences, dietary restrictions, and ingredient availability. However, achieving optimal results remains challenging due to the inherent complexity of natural language processing (NLP) in generating coherent, contextually relevant recipes. This paper investigates the prompt engineering techniques employed to enhance the accuracy and creativity of AI-based recipe generatio

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Arshdeep, Singh, and Abid Hussain Dr. "Prompt Engineering Unveiled: A Comprehensive Review of Innovative Strategies and Impactful Outcomes." Career Point International Journal of Research (CPIJR) 4, no. 2 (2024): 55–62. https://doi.org/10.5281/zenodo.11291223.

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Prompt engineering refers to the systematic design and optimization of input queries or instructions provided to natural language processing (NLP) models, with the goal of influencing their output. This abstract explores the significance of prompt engineering in enhancing the performance and interpretability of NLP models. By carefully crafting prompts, practitioners can guide models to produce desired responses, improve robustness, and mitigate biases. The abstract delves into various techniques and methodologies employed  in  prompt  engineering,  highlighting  its &

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Kamran, Muhammad, Muhammad Faizan, Shuhong Wang, Bowen Han, and Wei-Yi Wang. "Generative AI and Prompt Engineering: Transforming Rockburst Prediction in Underground Construction." Buildings 15, no. 8 (2025): 1281. https://doi.org/10.3390/buildings15081281.

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The construction industry is undergoing a transformative shift through automation, with advancements in Generative AI (GenAI) and prompt engineering enhancing safety and efficiency, particularly in high-risk fields like underground construction, geotechnics, and mining. In underground construction, GenAI-powered prompts are revolutionizing practices by enabling a shift from reactive to predictive approaches, leading to advancements in design, project planning, and site management. This study explores the use of Google Gemini, a recent advancement in GenAI, for the prediction of rockburst inten

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PremaLatha, V., Anguraj Dinesh Kumar, and Nikhat Parveen. "Optimizing interactions: Strategies for prompt engineering in large language models." Edu - Tech Enterprise 3 (April 15, 2025): 24. https://doi.org/10.71459/edutech202524.

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This manuscript delineates an innovative investigation into the rapidly evolving domain of prompt engineering, an essential competency in the contemporary landscape of sophisticated artificial intelligence, particularly concerning Large Language Models (LLMs) such as ChatGPT. Prompt engineering, defined as the meticulous formulation of precise and impactful prompts, is instrumental in directing LLMs to conform to explicit parameters, facilitate intricate procedures, and uphold the integrity of both the quality and quantity of their generated outputs. We present a groundbreaking aggregation of

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Bansal, Prashant. "Prompt Engineering Importance and Applicability with Generative AI." Journal of Computer and Communications 12, no. 10 (2024): 14–23. http://dx.doi.org/10.4236/jcc.2024.1210002.

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Tani, Toni, Antti Yläkujala, Lasse Metso, Tiina Sinkkonen, and Timo Kärri. "Prompt Engineering P2X Business Ecosystem with Generative AI." Procedia Computer Science 256 (2025): 20–27. https://doi.org/10.1016/j.procs.2025.02.091.

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Ilchenko, Mykhailo. "NEXT-GEN TELECOM AI: MASTERING PROMPT ENGINEERING FOR INNOVATION." Information and Telecommunication Sciences, no. 1 (June 24, 2025): 22–29. https://doi.org/10.20535/2411-2976.12025.22-29.

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Background. Since 2021, prompt engineering has emerged as a cornerstone of artificial intelligence (AI), revolutionising telecommunications by 2023 through optimised large language models (LLMs). Objective. This review synthesises existing research to evaluate prompt engineering’s transformative role in telecommunications, emphasising practical applications, technical challenges, and future directions. Methods. This analysis draws on 2021–2025 literature from 31 sources, including IEEE journals, ACM Transactions on Information Systems, NeurIPS proceedings, and arXiv preprints, examining prompt

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Söilen, Klaus Solberg. "A Review of the Knowledge Worker as Prompt Engineer: How Good is AI at Societal Analysis and Future Predictions?" Foresight and STI Governance 18, no. 2 (2024): 6–20. https://doi.org/10.17323/2500-2597.2024.2.6.20.

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What is the literature on AI missing for prompting engineering so far, and how good are these services at Societal Analysis and Future Predictions? A literature review and laboratory tests were conducted using different AI services. This study provides an extensive list of research gaps based on an analysis of existing literature. Furthermore, it demonstrates that AI with well-crafted prompts performs as well as or better than senior intelligence analysts in Societal Analysis and Future Predictions. The literature and analysis make it clear that the role of the prompter, to ensure reliability,

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ELMAS, Yunus. "Yabancı Dil Türkçe Öğretiminde İstem Mühendisliği Teknikleriyle Sohbet Robotlarının Kullanımı." International Journal of Social Sciences 8, no. 35 (2024): 54–83. http://dx.doi.org/10.52096/jcsl.05.09.04.

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Artificial intelligence is one of the most important technologies of today. With its increasing and diversifying applications, this technology holds significant potential in many fields, including education. Chatbots are large language models created to engage in conversations with humans by utilizing natural language processing technology, a subfield of artificial intelligence. In recent years, this area has made significant advancements, and chatbots capable of generating human-like texts and visuals have come to be known as generative AI models. Chatbots hold great potential in the teaching

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Researcher. "PROMPT ENGINEERING: REVOLUTIONIZING NATURAL LANGUAGE PROCESSING." International Journal of Artificial Intelligence and Machine Learning (IJAIML) 3, no. 2 (2024): 195–203. https://doi.org/10.5281/zenodo.13933394.

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Natural Language Processing (NLP) has undergone a revolution with the emergence of large language models (LLMs) like GPT and BERT. Prompt engineering, the art of crafting effective inputs to guide LLMs, has become a cornerstone of modern NLP applications. This paper explores how prompt engineering is streamlining NLP processes, revolutionizing industries such as banking, and addressing the inherent risks associated with generative AI. Through practical examples and a discussion of potential pitfalls, we offer strategies for mitigating challenges and maximizing the benefits of prompt engineerin

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Fatawi, Izzul, Muhammad Asy'ari, H. Hunaepi, Taufik Samsuri, and Muhammad Roil Bilad. "Empowering Language Models Through Advanced Prompt Engineering: A Comprehensive Bibliometric Review." Indonesian Journal of Science and Technology 9, no. 2 (2024): 441–62. https://doi.org/10.17509/ijost.v9i2.71481.

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This review examines the transformative impact of prompt engineering on language models in artificial intelligence. Language models have advanced from simple probabilistic frameworks to sophisticated neural networks like the Generative Pre-trained Transformer series, enhancing machine understanding and generating human-like language. Prompt engineering customizes these models for specific tasks, improving human-AI interaction. This technique has expanded the capabilities of language models, making them useful in healthcare for better diagnostics and in education for interactive learning. The r

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Li, Yannan. "Reduce AI Illusion Based on Data Science Technology and Prompt Engineering." Applied and Computational Engineering 97, no. 1 (2024): 152–56. http://dx.doi.org/10.54254/2755-2721/97/20241463.

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Abstract. Since the advent of artificial intelligence (AI) systems such as ChatGPT, artificial intelligence has been widely used with its advantages of low cost and efficiency, but it has also brought negative effects such as academic plagiarism and fake news, especially the "AI illusion". Data science improves the performance of large language models and reduces the generation of error information through cleaning, preprocessing, and data enhancement techniques. The research direction of this paper starts from the data level and the algorithm level. At the data level, this paper discusses dat

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Kim, Daejoong. "An Exploratory Study on Competency Development Effects of Generative AI Prompt Engineering Education." Korea Association for Care Competency Education 10, no. 1 (2025): 79–102. https://doi.org/10.52616/jccer.2025.10.1.79.

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Despite the popularization of generative AI technology since the advent of ChatGPT, the reality of insufficient systematic education has amplified the importance of prompt engineering training. This study explored the effects of generative AI prompt engineering education on students' core competencies in a university liberal arts education setting. Education on eight prompt engineering techniques was provided to 71 students enrolled in a liberal arts course at J University. Changes in their confidence in AI utilization, perception of prompt engineering, and eight core competencies (problem ess

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Bevara, Ravi Varma Kumar, Nishith Reddy Mannuru, Sai Pranathi Karedla, and Ting Xiao. "Scaling Implicit Bias Analysis across Transformer-Based Language Models through Embedding Association Test and Prompt Engineering." Applied Sciences 14, no. 8 (2024): 3483. http://dx.doi.org/10.3390/app14083483.

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In the evolving field of machine learning, deploying fair and transparent models remains a formidable challenge. This study builds on earlier research, demonstrating that neural architectures exhibit inherent biases by analyzing a broad spectrum of transformer-based language models from base to x-large configurations. This article investigates movie reviews for genre-based bias, which leverages the Word Embedding Association Test (WEAT), revealing that scaling models up tends to mitigate bias, with larger models showing up to a 29% reduction in prejudice. Alternatively, this study also undersc

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Sharma, Prof Sanjay Kumar. "Responsible Prompt Engineering: An Embedding Based Approach to Secure LLM Interactions." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1128–33. https://doi.org/10.22214/ijraset.2025.72157.

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Securing the interaction layer of large language models (LLMs) against emerging threats is paramount for their responsible and ethical deployment. These powerful AI systems, while offering flexibility, are highly susceptible to prompt injection, abuse and the generation of malicious behaviors. In this paper, we propose a novel, ethical governance approach for responsible prompt engineering, based on pre inference semantic threat detection. This methodology involves converting user prompts into semantically rich embedding vectors using Sentence Transformer (all-MiniLM-L6-v2). These vectors are

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Natarajan, Rajalakshmi Thiruthuraipondi. "Generative AI and Prompt Engineering in Operations using Oracle." Journal of Artificial Intelligence, Machine Learning and Data Science 2, no. 4 (2024): 2029–31. https://doi.org/10.51219/jaimld/rajalakshmi-thiruthuraipondi-natarajan/446.

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Deepak Pai. "Prompt Engineering Frameworks for Generative AI in Credit Analysis." Journal of Information Systems Engineering and Management 10, no. 45s (2025): 846–52. https://doi.org/10.52783/jisem.v10i45s.9035.

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The financial services industry is currently in the midst of a significant transformation, largely sparked by the rise and widespread integration of generative artificial intelligence (GenAI) technologies. These tools and technologies available have the potential to fundamentally change how we operate and make strategic decisions. In this fast-paced environment, generative AI’s knack for synthesizing, analyzing, and extracting insights from extensive and varied datasets is reshaping credit analysis. Traditionally, this field has leaned on careful data examination, statistical modeling, and the

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Alemanno, Antonio, Michele Carmone, and Leonardo Priore. "Prompting for Healthcare Professionals: Enhancing Clinical Decision-Making with Artificial Intelligence." infermieristica journal 4, no. 1 (2025): 33–39. https://doi.org/10.36253/if-3198.

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Introduction. Generative Artificial Intelligence (AI), specifically through Large Language Models (LLMs), is progressively reshaping clinical documentation, decision support, patient education, and research synthesis in healthcare. Despite significant benefits, these models pose challenges such as inaccuracies (hallucinations) and inherent biases. This paper highlights prompt engineering as an emerging and critical skill for healthcare professionals and demonstrates how structured prompting techniques can improve the reliability, clinical relevance, and ethical compliance of AI-driven applicat

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Dr, K. Balaji, Lokesha Akshatha, G. Chandana, and H. M. Pushpa. "Optimizing Prompt Length and Specificity for Enhanced AI Chatbot Responses." International Journal of Current Science Research and Review 07, no. 09 (2024): 7421–28. https://doi.org/10.5281/zenodo.13860920.

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Abstract : <strong>L</strong>anguage models have revolutionized natural language processing by greatly improving text generation and comprehension. Optimizing their functioning is related to how one designs prompts because the kind and quality of response produced affects the nature of response that is generated. This article explores the impact of prompt length and specificity on AI chatbots’ capabilities concerning accuracy, fluency, and relevance of generated responses. We present evidence that careful prompt engineering is severely lacking to improve conversational performance, and i

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R. Brawn, James. "In Search of the Prompt that Produces useful Written Corrective Feedback for L2 Composition Classes." International Journal of Education (IJE) 12, no. 4 (2024): 17–24. https://doi.org/10.5121/ije2024.12402.

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The use of artificial intelligence (AI) in language education may be in its infancy, but technological advances, especially natural language processing, will lead to its widespread adoption far sooner than many may think. For example, large language models (LLMs) like ChatGPT are often used when individuals utilize AI systems. This means that researchers in second language learning must begin evaluating the utility of AI-based tools for second language instruction. This study describes the importance of prompt engineering in designing effective prompts for second-language writing feedback. Thi

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Dr.A.Shaji, George, George A.S.Hovan, and Dr.T.Baskar. "Exploring the Potential of Prompt Engineering in India: A Study on the Future of AI-Driven Job Market and the Role of Higher Education." Partners Universal Innovative Research Publication (PUIRP) 01, no. 02 (2023): 34–57. https://doi.org/10.5281/zenodo.10121998.

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The advent of artificial intelligence (AI) has spurred the emergence of unique professions, one of which is prompt engineering. This research paper titled "Exploring the Potential of Prompt Engineering in India: A Study on the Future of the AI-Driven Job Market and the Role of Higher Education" delves into the potential of India to become a global hub for prompt engineers. Prompt engineering, a novel field that combines natural language processing, machine learning, creativity, and linguistic expertise, involves designing effective prompts for large language models (LLMs) like ChatGPT. These m

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