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.

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.

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...

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.

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...

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 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.

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.

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.

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 paper presents the most comprehensive survey on prompt engineering to date, assembling a taxonomy of prompting techniques and analyzing their applications, and presents a meta-analysis of the entire literature on natural language prefix-prompting.

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.

Large Language Models (LLMs) have recently demonstrated successes in a broad range of areas that require language understanding and generation. The design of course curricula is a key part of the educational process, as it helps students understand expectations and learning goals, and teachers to maintain consistency when conducting the course. Thus, there would be benefits if educators could leverage the capabilities of LLMs. The first benefit is the potential for improving course content, which would lead to better learning outcomes for students. Secondly, educators can save time by having a...

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.

Prompt engineering has become a vital technique in artificial intelligence (AI), enhancing interactions with large language models (LLMs) and vision-language models (VLMs). By strategically crafting prompts, this approach improves AI performance across domains such as natural language processing (NLP), computer vision (CV), and healthcare. Techniques like zero-shot, few-shot, chain-of-thought (CoT), and retrieval-based prompting refine model responses, increasing accuracy and efficiency. Hard and soft prompting methods play distinct roles, balancing interpretability and customization. Applicat...

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.

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...

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.

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.

With the right approach, nurses can use AI to benefit the profession and patient care.

This playful method provides a new approach to teaching artificial intelligence technologies in higher engineering education, promoting student interaction and engagement.

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.

The purpose of this paper is to study the topic of prompt engineering, which serves as a valuable tool for teachers in creating optimal prompts that effectively enhance students' learning experiences with ChatGPT. This paper explores a variety of strategies related to prompt engineering. These strategies include assigning specific roles to ChatGPT, clearly defining objectives, applying constraints, utilizing structural prompt formats, refining answers through dialogues, and integrating practice exercises. Moreover, this paper specifically delves into relevant approaches to prompt engineering i...

This study explores techniques that enhance the usability and reliability of LLMs, emphasizing the ongoing importance of well-designed prompts in optimizing task performance and highlighting the critical role of prompt patterns in maximizing LLM’s potential, even as their capabilities continue to evolve.

Large Language Models (LLMs) are increasingly integrated into software applications, with prompts serving as the primary 'programming' interface to guide their behavior. As a result, a new software paradigm, promptware, has emerged, using natural language prompts to interact with LLMs and enabling complex tasks without traditional coding. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are pr...

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.

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.

This is the first of a series of short reports that seek to help business, education, and policy leaders understand the technical details of working with AI through rigorous testing. In this report, we demonstrate two things: - There is no single standard for measuring whether a Large Language Model (LLM) passes a benchmark, and that choosing a standard has a big impact on how well the LLM does on that benchmark. The standard you choose will depend on your goals for using an LLM in a particular case. - It is hard to know in advance whether a particular prompting approach will help or harm the ...

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.

There appears to be a specificity range, across all considered models, where the LLM performs the best, suggesting that manipulating prompts within this range could maximize LLM performance and lead to more e ” cient applications in specialized domains.

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.

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.

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.

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.

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

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.

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.

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

The utility model provides an engine oil change prompt device, comprising at least one engine oil index detector, a controller and a prompt device that more accurately judges the quality of the engine oil according to the detected engine oil and timely informs the driver.

The idea that many librarians already have the knowledge, skills, abilities and aptitude to do PE is introduced to challenge librarians to consider how it relates to the work that they are doing and consider if it might enhance their current ability to serve users.

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.

A linguistic perspective on the study of the interaction between humans and Artificial Intelligence is offered by studying, identifying, and describing the linguistic features of prompts and their parameters that influence the stylistic correctness of the generated responses by the neural networks.

In tasks related to creative writing or content generation, individuals can employ prompt chaining to construct a narrative, shape characters, or explore various storytelling avenues. AI has become a key element in content creation, transforming how content is generated, optimized, and refined. Prompt engineering is the meticulous crafting and optimization of queries or instructions to elicit precise and valuable responses from generative AI models. This strategic approach translates human intentions and business needs into actionable outcomes, ensuring alignment with desired objectives. The p...

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.

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.

A catalog of prompt engineering techniques, presented in pattern form, that have been applied to solve common problems when conversing with LLMs to aid software development tasks are described.

A pioneering taxonomy offers a foundational framework that clarifies the roles of prompt engineering and measures its impact, thereby guiding evolving AI -driven software engineering research and practices.