Prompt Engineering: Guiding the Way to Effective Large Language Models

被引：0

作者：

Aljanabi M. ^{[1
]}

Yaseen M.G. ^{[2
]}

Ali A.H. ^{[1
]}

Mohammed M.A. ^{[2
]}

机构：

[1] Department of Computer, College of Education, Al-Iraqia University, Baghdad

来源：

Iraqi Journal for Computer Science and Mathematics | 2023年 / 4卷 / 04期

关键词：

Editorial; Large Language model; Prompt engineering;

D O I：

10.52866/ijcsm.2023.04.04.012

中图分类号：

学科分类号：

摘要：

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. © 2023 College of Education, Al-Iraqia University. All rights reserved

引用

页码：151 / 155

页数：4

共 10 条

[1] Heston T. F., Khun C., Prompt engineering in medical education, International Medical Education, 2, 3, pp. 198-205, (2023)
[2] Yong G., Jeon K., Gil D., Lee G., Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model, Computer‐Aided Civil and Infrastructure Engineering, 38, 11, pp. 1536-1554, (2023)
[3] Gu J., Et al., A systematic survey of prompt engineering on vision-language foundation models, (2023)
[4] Henrickson L., Merono-Penuela A., Prompting meaning: a hermeneutic approach to optimising prompt engineering with ChatGPT, AI & SOCIETY, pp. 1-16, (2023)
[5] Busch K., Rochlitzer A., Sola D., Leopold H., Just tell me: Prompt engineering in business process management, International Conference on Business Process Modeling, Development and Support, pp. 3-11, (2023)
[6] Lee U., Et al., Few-shot is enough: exploring ChatGPT prompt engineering method for automatic question generation in english education, Education and Information Technologies, pp. 1-33, (2023)
[7] Zhang C., Et al., A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering, (2023)
[8] Addlesee A., Sieinska W., Gunson N., Garcia D. H., Dondrup C., Lemon O., Multi-party goal tracking with llms: Comparing pre-training, fine-tuning, and prompt engineering, (2023)
[9] Heston T. F., Khun C., Prompt engineering in medical education, J International Medical Education, 2, 3, pp. 198-205, (2023)
[10] Oppenlaender J., Prompt engineering for text-based generative art, (2022)

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