Just Tell Me: Prompt Engineering in Business Process Management

Busch, Kiran; Alexander, Rochlitzer,; Sola, Diana; Leopold, Henrik

doi:10.1007/978-3-031-34241-7_1

Cited by 30 publications

(4 citation statements)

References 13 publications

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“…However, research has concentrated mainly on practice, research, teaching, and scientific publication in health. Consequently, there is a gap when considering the opportunities of using GPT and conversational assistants in engineering; a similar conclusion is achieved in [10] by analyzing the potential uses of LLM in business process management.…”

Section: Introductionmentioning

confidence: 64%

Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Velásquez-Henao,

Franco-Cardona,

Cadavid-Higuita

2023

DYNA

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ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering.

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Section: Introductionmentioning

confidence: 64%

Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Velásquez-Henao,

Franco-Cardona,

Cadavid-Higuita

2023

DYNA

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“…For instance, it is not always clear that longer prompts yield better performance than short prompts, even though the former is more expensive (involving more tokens) than the latter. Prompt engineering has been actively researched in domains ranging from graph analytics [21] and healthcare [41] to business process management [7], but to our knowledge, remains to be systematically studied in ER.…”

Section: Prompt Engineering Methods For Ermentioning

confidence: 99%

Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution

Nananukul,

Sisaengsuwanchai,

Kejriwal

2024

Preprint

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Entity Resolution (ER) is the problem of semi-automatically determining when two entities refer to the same \emph{underlying} entity, with applications ranging from healthcare to e-commerce. Traditional ER solutions required considerable manual expertise, including domain-specific feature engineering, as well as identification and curation of training data. Recently released large language models (LLMs) provide an opportunity to make ER more seamless and domain-independent. However, it is also well known that LLMs can pose risks, and that the quality of their outputs can depend on how prompts are engineered. Unfortunately, a systematic experimental study on the effects of different prompting methods for addressing unsupervised ER, using LLMs like ChatGPT, has been lacking thus far. This paper aims to address this gap by conducting such a study. We consider some relatively simple and cost-efficient ER prompt engineering methods and apply them to ER on two real-world datasets widely used in the community. We use an extensive set of experimental results to show that an LLM like GPT3.5 is viable for high-performing unsupervised ER, and interestingly, that more complicated and detailed (and hence, expensive) prompting methods do not necessarily outperform simpler approaches. We provide brief discussions on qualitative and error analysis, including a study of the inter-consistency of different prompting methods to determine whether they yield stable outputs. Finally, we consider some limitations of LLMs when applied to ER.

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“…The concept of "chain-of-thought" refers to the sequential progression of ideas or thoughts the aforementioned approach entails the deconstruction of intricate activities into smaller, more feasible components. The provision of a systematic guide enables the LLM to engage in a more methodical approach to reasoning and problem-solving, particularly when faced with activities that necessitate logical thinking or sequential processing [5]. The concept of few-shot learning refers to the ability of a machine learning model to learn and the process of learning in the context of the LLM involves the utilization of a set of desired outputs as exemplars.…”

Section: Techniques For Prompt Engineeringmentioning

confidence: 99%

Prompt Engineering: Guiding the Way to Effective Large Language Models

Aljanabi,

Yaseen,

Ali

et al. 2023

ijcsm

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Large language models (LLMs) have become prominent tools in various domains, such as natural language processing, machine translation, and the development of creative text. Nevertheless, in order to fully exploit the capabilities of Language Models, it is imperative to establish efficient communication channels between humans and machines. The discipline of engineering involves the creation of well-constructed and informative prompts, which act as a crucial link between human intention and the execution of tasks by machines. The present study examines the concept of rapid engineering, elucidating its underlying concepts, methodologies, and diverse range of practical applications.

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Just Tell Me: Prompt Engineering in Business Process Management

Cited by 30 publications

References 13 publications

Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution

Prompt Engineering: Guiding the Way to Effective Large Language Models

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