Techniques and Applications in Prompt Engineering and Generative AI

Dear Colleagues,

Prompt engineering is a dynamic field that focuses on the development and optimization of generative AI chatbot prompts. In this regard, prompt engineering includes the development of algorithms and procedures for creating effective prompts, methods for evaluating the effectiveness of prompts, and the investigation of real-world case studies that demonstrate the benefits of prompt engineering applications. By advancing these areas, we aim to improve the functionality and usability of AI-driven programming tools.

Submissions may include research on advanced prompt design techniques, frameworks for evaluating prompt effectiveness, and detailed case studies of successful implementations in programming environments. In addition, we encourage interdisciplinary research that bridges the gap between rapid engineering and many other fields, including education, by providing insights into optimizing AI tools for both educators and learners.

The aim of this Special Issue is to identify the potential for prompt engineering in AI, not only in the educational tools but also in automated software development, information retrieval, natural language processing, improving collaboration in software development projects, as well as ethical considerations for prompt engineering and privacy concerns.

We encourage submissions that not only propose novel theories, but also implement and experimentally validate these concepts to ensure their practical applicability and reproducibility by other researchers.

The topics covered include, but are not restricted to, the following:

• Advanced techniques for designing effective programming prompts;
• Evaluation methodologies for prompt efficacy;
• Case studies of prompt engineering in real-world programming scenarios;
• The use of prompt engineering techniques in copilot-based coding assistants;
• Educational applications of prompt engineering;
• Frameworks for teaching prompt engineering in it and computer science courses;
• Interdisciplinary approaches to prompt engineering and education;
• Automated programming tools and how they depend on prompt engineering;
• Ethical considerations for prompt engineering;
• Prompt engineering to improve collaboration in software development projects;
• Prompt engineering and domain-specific fine-tuning of large language models;
• AI and privacy.

Dr. Marko Horvat
Dr. Tomislav Jagušt
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• generative AI
• prompt engineering
• copilot-based coding assistants
• automated programming tools
• evaluation methodologies