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Relevant bibliographies by topics / AI Prompt Engineering

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Academic literature on the topic 'AI Prompt Engineering'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "AI Prompt Engineering"

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 article addresses this educational gap by providing an overview of generative AI prompt engineering, its potential applications in primary care medicine, and best practices for its effective implementation. The role of well-crafted prompts in eliciting accurate, relevant, and valuable responses from AI models is discussed, emphasizing the need for prompts grounded in medical knowledge and aligned with evidence-based guidelines. The article explores various applications of prompt engineering in primary care, including enhancing patient–provider communication, streamlining clinical documentation, supporting medical education, and facilitating personalized care and shared decision-making. Incorporating domain-specific knowledge, engaging in iterative refinement and validation of prompts, and addressing ethical considerations and potential biases are highlighted. Embracing prompt engineering as a core competency in medical education will be crucial for successfully adopting and implementing AI technologies in primary care, ultimately leading to improved patient outcomes and enhanced healthcare delivery.

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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 patterns empower students to optimize their AI interactions, thus enhancing both their learning and problem-solving capabilities.

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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, and more individualized learning. Teachers also stand to gain from AI-based automation, which facilitates the simplification of assessment and curriculum design complexity. The research also touches on the question of challenges faced in using AI-based prompt engineering, such as ethics, overdependence on AI by students, and data privacy. In the main, the study illustrates the revolutionizing significance of prompt engineering in contemporary learning to provide prophetic teaching styles to amplify learners' knowledge gain.

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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 the complexities of human-AI interaction and the evolving demands of technology, this research contributes to the ongoing discourse on the professionalization of emerging disciplines

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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 complexities ofhuman-AI interaction and the evolving demands of technology, this research contributes to the ongoingdiscourse on the professionalization of emerging disciplines

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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 complexities ofhuman-AI interaction and the evolving demands of technology, this research contributes to the ongoingdiscourse on the professionalization of emerging disciplines.

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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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10

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 consistency. The findings demonstrate that well-crafted prompts significantly enhance LLM output quality across various domains, including healthcare, finance, and education. The article also reveals emerging trends in automated prompt generation and multimodal applications, suggesting future directions for prompt engineering development. This article contributes to the growing knowledge in AI interaction optimization and provides practical guidelines for implementing effective prompt engineering strategies in conversational AI systems.

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Books on the topic "AI Prompt Engineering"

1

AI Basics and the RGB Prompt Engineering Model: Empowering AI & ChatGPT Through Effective Prompt Engineering. Praizion Media, 2023.

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Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs. O'Reilly Media, Incorporated, 2024.

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AI Unleashed: Prompt Engineering and Development for Business Transformation. Pearson Media, 2023.

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AI Unleashed: Prompt Engineering and Development for Business Transformation. Pearson Media, 2023.

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AI Unleashed: Prompt Engineering and Development for Business Transformation. Pearson Media, 2023.

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Balk, Igor. Mastering ChatGPT Prompt Engineering: A Comprehensive Guide to Conversational AI. Global Innovation Labs LLC, 2023.

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Balk, Igor. Mastering ChatGPT Prompt Engineering: A Comprehensive Guide to Conversational AI. Global Innovation Labs LLC, 2023.

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Bozkurt. Transforming Education with Generative AI: Prompt Engineering and Synthetic Content Creation. IGI Global, 2024.

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Bozkurt. Transforming Education with Generative AI: Prompt Engineering and Synthetic Content Creation. IGI Global, 2024.

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Bozkurt. Transforming Education with Generative AI: Prompt Engineering and Synthetic Content Creation. IGI Global, 2024.

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Book chapters on the topic "AI Prompt Engineering"

1

McTear, Michael, and Marina Ashurkina. "Advanced Prompt Engineering." In Transforming Conversational AI. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_6.

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McTear, Michael, and Marina Ashurkina. "Introduction to Prompt Engineering." In Transforming Conversational AI. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0110-5_5.

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Kansal, Aarushi. "Prompt Engineering Techniques." In Building Generative AI-Powered Apps. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0205-8_8.

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Huang, Ken, Grace Huang, Yuyan Duan, and Ju Hyun. "Utilizing Prompt Engineering to Operationalize Cybersecurity." In Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_9.

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Bjerg, Jonas. "Tips and Tricks for Prompt Engineering." In The Early-Career Professional’s Guide to Generative AI. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0456-4_10.

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Wang, Xiaofeng, Mohammad Idris Attal, Usman Rafiq, and Sylvia Hubner-Benz. "Turning Large Language Models into AI Assistants for Startups Using Prompt Patterns." In Agile Processes in Software Engineering and Extreme Programming – Workshops. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48550-3_19.

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AbstractMost startups operate with limited resources and experience. AI technologies enable them to accomplish many tasks under these constraints. The recent advance of large language models (LLMs) offers new opportunities to support startup endeavors. Given the nascent nature of LLMs, how they could be utilized to support startups is yet to be investigated. Since prompt engineering is believed to be at the core of the effective use of LLMs, we aim to understand how to apply prompt engineering to turn LLMs into AI assistants for startups. As the first step, we investigated the application of a set of prompt patterns to ChatGPT, arguably the most widely known LLM currently. The preliminary results show that some patterns are more suitable for brainstorming which is a typical activity conducted by early-stage startups. Prompt-tuned questions may lead to more specific and more detailed responses, but it is not guaranteed. Meantime, human factors play an important role in the effective application of prompt patterns. Large-size and systematic studies are needed to apply the right patterns to different questions, taking into account the differences among startups in terms of their startup knowledge, domain knowledge, and their attitudes and behaviors towards LLMs.

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Sainio, Kari, Pekka Abrahamsson, and Tero Ahtee. "Prompt Patterns for Agile Software Project Managers: First Results." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53227-6_14.

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AbstractIn the evolving field of Agile Project Management (APM), the role of the project manager is in transition. This paper identifies common ‘pain points’ in APM through a literature review and constructs a theoretical model to address them. The study introduces ‘Prompt Engineering’ as a novel approach to leverage artificial intelligence (AI), specifically ChatGPT, for mitigating these challenges. Empirical research evaluates ChatGPT's capabilities and reliability in managing various project tasks using engineered prompts. The findings suggest that while ChatGPT cannot fully replace human project managers, it excels in assisting, guiding, and automating specific tasks when guided by well-crafted prompts. As an outcome, prompt engineering patterns for project managers is proposed to facilitate the application of AI in agile settings. In this paper, we introduce patterns for requirements management, stakeholder and management teams and role clarification. The paper concludes that ChatGPT's knowledge is generally reliable but emphasizes the need for expert evaluation in critical areas.

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Mohsenimofidi, Seyedmoein, Akshy Sripad Raghavendra Prasad, Aida Zahid, Usman Rafiq, Xiaofeng Wang, and Mohammad Idris Attal. "Classifying User Intent for Effective Prompt Engineering: A Case of a Chatbot for Startup Teams." In Generative AI for Effective Software Development. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55642-5_15.

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Piller, Christoph. "Meta-prompt Engineering in ChatGPT-4 for AI-Generated BPM Reference Models." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72041-3_22.

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Geroimenko, Vladimir. "Generative AI Hallucinations in Healthcare: A Challenge for Prompt Engineering and Creativity." In Springer Series on Cultural Computing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86551-0_17.

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Conference papers on the topic "AI Prompt Engineering"

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Young, Darrell L., Eric C. Larson, and Mitchell A. Thornton. "Prompt engineering for detecting phishing." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3055277.

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Deshmukh, Rushali, Rutuj Raut, Mayur Bhavsar, Sanika Gurav, and Yash Patil. "Optimizing Human-AI Interaction: Innovations in Prompt Engineering." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914815.

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Li, Jinyang, Sangwon Hyun, and M. Ali Babar. "DDPT: Diffusion-Driven Prompt Tuning for Large Language Model Code Generation." In 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE, 2025. https://doi.org/10.1109/cain66642.2025.00030.

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Kale, Atharva, Tanay Yaduka, Tauhid Shaikh, et al. "Unveiling the Power of AI Prompt Engineering: A Comprehensive Exploration." In 2024 10th International Conference on Electrical Energy Systems (ICEES). IEEE, 2024. https://doi.org/10.1109/icees61253.2024.10776884.

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Jayaram, M., Yogesh Bhutkar, Indhra Lochan Kumar Bojjanapalli, Giri Yeshwanth, and Burri Yashwanth Reddy. "Beyond Automation: AI-Driven Project Management with OpenAI and Prompt Engineering." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698333.

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Bruni, Marc, Fabio Gabrielli, Mohammad Ghafari, and Martin Kropp. "Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Models." In 2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge). IEEE, 2025. https://doi.org/10.1109/forge66646.2025.00018.

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Nair, Amrutha Muralidharan, Akshara Balan, Alan C M, Maria Baiju, and Tom Davis. "Towards Secure AI: Detection of Prompt Injection Attacks with Explainability." In 2025 2nd International Conference on Trends in Engineering Systems and Technologies (ICTEST). IEEE, 2025. https://doi.org/10.1109/ictest64710.2025.11042369.

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Kim, Hannah, Hyun Lee, Sunyu Pang, and Uran Oh. "Prompirit: Automatic Prompt Engineering Assistance for Improving AI-Generated Art Reflecting User Emotion." In 2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2024. http://dx.doi.org/10.1109/iri62200.2024.00038.

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Ahmed, Shihab, A. B. M Mohaimenur Rahman, Md Morshed Alam, and Md Sajidul Islam Sajid. "SPADE: Enhancing Adaptive Cyber Deception Strategies with Generative AI and Structured Prompt Engineering." In 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2025. https://doi.org/10.1109/ccwc62904.2025.10903748.

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Han, Yuna, Simon Shim, Deva Kumar Gajulamandyam, and Hangbae Chang. "Prompt-Based Generation Strategy for Imbalanced Information Security Rating Dataset Augmentation." In 2024 International Conference on AI x Data and Knowledge Engineering (AIxDKE). IEEE, 2024. https://doi.org/10.1109/aixdke63520.2024.00029.

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Reports on the topic "AI Prompt Engineering"

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Ravi Krishnan, Prathiksha. Comparative Analysis of Prompt Engineering and Their Applications in Agricultural AI Systems. Iowa State University, 2024. https://doi.org/10.31274/cc-20250502-112.

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Fariborzi, Hadi, and Piers Steel. New ChatGPT and AI Tools for Academic Research and Publishing. Instats Inc., 2024. http://dx.doi.org/10.61700/wdf7gomkxo8yh1080.

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This transformative half-day seminar is designed to equip participants with the latest AI tools and techniques to assist the process of academic research and publishing. Perfect for both newcomers and seasoned AI users, this seminar promises to revolutionize your approach to scholarly work by using AI tools as research assistants. The seminar is comprised of four separate sessions, each designed to explore different facets of AI in academic research, including: revolutionizing research with AI tools and technologies; mastering prompt engineering; navigating the ethics and security of AI; and applying AI in advanced research and writing.

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