Top Research Papers on Prompt Engineering

Seit Ende 2022 haben ChatGPT und Co. mehrere Millionen Nutzer*innen gewinnen können – zu diesem Zeitpunkt bekam die breite Masse Zugang zu künstlicher Intelligenz, vor allem aber zu generativer KI. Expert*innen erklärten sich den Hype um die App vor allem durch die einfache Nutzbarkeit: Auch ohne Computer-Kenntnisse zu besitzen, konnten Laien nun einfach mit KI interagieren. Für eine effiziente Nutzung ist es jedoch notwendig, die richtige Handhabung für die Verwendung der großen Sprachmodelle zu finden.

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.

Various applications of generative AI prompt engineering in primary care are explored, including enhancing patient–provider communication, streamlining clinical documentation, supporting medical education, and facilitating personalized care and shared decision-making.

This work infuses into the meta-prompt three key components: detailed descriptions, context specification, and a step-by-step reasoning template that exhibits remarkable versatility across diverse language tasks.

Purpose In terms of training the public in prompt engineering skills, no single discipline or profession currently takes the lead, presenting an opportunity for professions like librarianship to step into this role. Librarians are already well-equipped to educate the public in a wide range of literacy skills and tasks, so prompt engineering may be a natural progression. The purpose of this paper is to examine the potential role of prompt engineering for library professionals. Design/methodology/approach Prompt engineering is the process of optimizing the text that is provided to an artifici...

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...

The study categorizes prompt engineering techniques into instruction-based, information-based, reformulation, and metaphorical prompts, and addresses ethical considerations in prompt engineering, emphasizing the need to mitigate bias and discrimination while ensuring transparency.

In the context of biomedical engineering, prompt engineering can be used to generate new ideas for research, design new medical devices, and improve the accuracy of clinical diagnoses.

It is demonstrated that a simple greedy approach with beam search outperforms other methods in terms of search efficiency and two novel techniques that utilize search history to enhance the effectiveness of LLM-based mutation in the authors' search algorithm are introduced.

This study aims to address a void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering, and combines insights from various methodologies to address issues like hallucinations.

This task hopes to spark conversation around the role of computational creativity research in the new world of generative deep learning, and vice versa.

By mastering prompt engineering, educators can leverage AI tools as powerful aids, potentially significantly enhancing teaching effectiveness, work efficiency, and student learning outcomes.

Conversational Prompt Engineering is proposed, a user-friendly tool that helps users create personalized prompts for their specific tasks, using a chat model to briefly interact with users, helping them articulate their output preferences and integrating these into the prompt.

This pictorial presents an ongoing research programme comprising three practice-based Design Research projects conducted through 2024, exploring the affordances of diffusion-based AI image generation systems, specifically Stable Diffusion. The research employs tangible and embodied interactions to investigate emerging qualitative aspects of generative AI, including uncertainty and materiality. Our approach leverages the flexibility and adaptability of Design Research to navigate the rapidly evolving field of generative AI. The pictorial proposes the notion of prompt craft as a productive refra...

This paper proposes a conceptual framework for PE as the skill of articulating a problem, its context, and the constraints of the desired solution to an AI assistant, ensuring a swift and accurate response.

Experimental results on several datasets derived from public sources indicate that the proposed approach achieves high-quality prompt recovery and generates prompts more similar to the originals than current state-of-the-art methods.

It is hypothesized that the way in which users iterate on their prompts can provide insight into how they think prompting and models work, as well as the kinds of support needed for more efficient prompt engineering.

The principles of prompt engineering, applied properly, are demonstrated to be effective in teaching across the diverse fields of anatomy, physiology, pathology, pharmacology, and clinical skills.

This position paper is used to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.

This paper proposes applying model-driven engineering to support the prompt engineering process using a domain-specific language (DSL), and defines platform-independent prompts that can later be adapted to provide good quality outputs in a target AI system.