Top Research Papers on Prompt Engineering

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.

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.

A catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs to improve the outputs of LLM conversations is described.

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.

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...

It is contended that prompt patterns play an essential role in providing the foundation for prompt engineering, which is a more disciplined and repeatable means of interacting with and evaluating LLMs.

It is found that participants could evaluate prompt quality and crafted descriptive prompts, but they lacked style-specific vocabulary necessary for effective prompting, in line with the hypothesis that prompt engineering is a new type of skill that is non-intuitive and must first be acquired before it can be used.

A new methodology inspired by codebook construction through qualitative methods is presented that lays a foundation for more systematic, objective, and trustworthy way of applying LLMs for analyzing data.

This study presents Prompt-Eng, a novel framework emphasizing its wide-ranging applications in healthcare, where precise prompts with positive and negative aspects are designed, and it is hypothesize that designing prompts in pairs helps models to generalize effectively.

To harness the full potential of LLMs in such crucial contexts, there needs to be a systematic, disciplined approach to "prompt engineering" that guides interactions with and evaluations of these LLMs.

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...

The development of prompt engineering will be provided and its significant contributions to healthcare natural language processing applications such as question-answering systems, text summarization, and machine translation will be emphasized.

A new approach to code generation by LLMs is proposed, which is called AlphaCodium - a test-based, multi-stage, code-oriented iterative flow, that improves the performances of LLMs on code problems.

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.

The process of prompt engineering to extract link predictions from an LLM is explored, providing detailed insights into the approach for constructing effective prompts, and multiple strategies for leveraging LLMs to generate traceability links are proposed.

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

A range of recent prompt engineering techniques for GPT-based Codenames agents are compared and qualitative changes in agents’ strategies are observed suggesting that further refinement has potential for score improvement.

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.

A systematic overview of the privacy protection methods employed during ICL and prompting in general is provided and a detailed examination of the promising areas that necessitate further exploration is offered.

This paper evaluates the effectiveness of the 5 prompt patterns' ability to make GPT-3.5 turbo perform the selected RE tasks and offers recommendations on which prompt pattern to use for a specific RE task and provides an evaluation framework as a reference for researchers and practitioners who want to evaluate different prompt patterns for different RE tasks.

The study underscores the importance of prompt structures in jailbreaking LLMs and discusses the challenges of robust jailbreak prompt generation and prevention.

Techniques for measuring the effect that specific words and phrases in prompts have, and guidance on the selection of prompts to produce desired effects are presented.

The role of “prompt engineers” as a professional title is explored, extending beyond the field of generative AI for developers, comparing certain tasks to the role of librarians, such as conducting search queries.

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.

Core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents are introduced and a survey of tools for prompt engineers is provided.

The results show that reasonable prompt templates can effectively improve the efficiency of automatic vulnerability repair, which is significantly improved compared with neural machine translation technology.

It is shown that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts.

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...

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.

It is hoped that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.

This work views LLMs like crowd workers and leverage ideas from the declarative crowdsourcing literature-including leveraging multiple prompting strategies, ensuring internal consistency, and exploring hybrid-LLM-non-LLm approaches-to make prompt engineering a more principled process.

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.

ControlPE enables finer adjustments to prompt effects, complementing existing prompt engineering, and effectively controls continuous targets, proving to be a promising solution for control a variety of prompts.

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

This research proposes PromptMagician, a visual analysis system that helps users explore the image results and refine the input prompts, and introduces a multi-level visualization for the cross-modal embedding of the retrieved images and recommended keywords.

This short paper identifies five types of prompt modifiers used by practitioners in the community of text-based generative art based on a 3-month ethnographic study on Twitter.

This paper summarizes the current state of research about prompt engineering and aims at providing practical recommendations for the wide range of health care professionals to improve their interactions with LLMs.

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.

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A systematic analysis of recent advancements in prompt engineering enables a better understanding of this rapidly developing field and facilitates future research by illuminating open challenges and opportunities for prompt engineering.

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This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models, image-text matching models, and text- to-image generation models.

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 pioneering research introduces the Automatic Prompt Engineering Toolbox (APET), which enables GPT-4 to autonomously apply prompt engineering techniques, resulting in substantial improvements in tasks like Word Sorting and Geometric Shapes.

Changes in morphology and lexicon, i.e., the vocabulary used, showed promise in improving prompts and contribute to developing more robust language models capable of handling variability in linguistic expression.

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.

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 framework provides a unified mathematical structure that not only systematizes the existing PE methods but also sets the stage for rigorous analytical improvements and extends this framework to include PE via ensemble methods and multi-agent collaboration, thereby enlarging the scope of applicability.

This work investigates the performance of zero-shot LPE for given facts in case-texts from the European Court of Human Rights and the Federal Supreme Court of Switzerland.

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.