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Research papers on Prompt engineering

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  1. Unleashing the potential of prompt engineering for large language models

    B.‐C. CHEN, Zhaofeng Zhang, Nicolas Langrené, et al. · 2025 · Patterns · 503 citations

    This review explores the role of prompt engineering in unleashing the capabilities of large language models (LLMs). Prompt engineering is the process of structuring inputs, and it has emerged as a crucial technique for maximizing the utility and accuracy of these models. Both foundational and advanced prompt engineering methodologies-including techniques such as self-consistency, chain of thought, and generated knowledge, which can significantly enhance the performance of models-are explored in this paper. Additionally, the prompt methods for vision language models (VLMs) are examined in detail. Prompt methods are evaluated with subjective and objective metrics, ensuring a robust analysis of

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  3. Improving large language models for clinical named entity recognition via prompt engineering

    Yan Hu, Qingyu Chen, Jingcheng Du, et al. · 2023 · Journal of the American Medical Informatics Association · 310 citations

    IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets. OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to ext

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  7. Integrating chemistry knowledge in large language models via prompt engineering

    Hongxuan Liu, Haoyu Yin, Zhiyao Luo, et al. · 2024 · Synthetic and Systems Biotechnology · 56 citations

    This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains. The proposed domain-knowledge embedded prompt engineering method outperforms traditional prompt engineering strategies on various metrics, including capability, accuracy, F1 score, and hallucination drop. The effectiveness of the method is demonstrated through case studies on complex materials including the MacMillan catalyst, paclitaxel, and lithium cobalt oxide. The results suggest that domain-knowledge prompts can guide LLMs to generate more accurate and relevant responses, highlighting the potential of LLMs as

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  8. Prompt Engineering for Large Language Models in Interventional Radiology

    Nicholas Dietrich, Nellie Bradbury, Christopher Loh · 2025 · American Journal of Roentgenology · 24 citations

    Prompt engineering plays a crucial role in optimizing artificial intelligence (AI) and large language model (LLM) outputs by refining input structure, a key factor in medical applications where precision and reliability are paramount. This Clinical Perspective provides an overview of prompt-engineering techniques and their relevance to interventional radiology (IR). It explores key strategies, including zero-shot, one-or few-shot, chain-of-thought, tree-of-thought, self-consistency, and directional stimulus prompting, showing their application in IR-specific contexts. Practical examples illustrate how these techniques can be effectively structured for workplace and clinical use. Additionally

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  9. Autonomous Prompt Engineering in Large Language Models

    Daan Kepel, Konstantina Valogianni · 2024 · arXiv (Cornell University) · 9 citations

    Prompt engineering is a crucial yet challenging task for optimizing the performance of large language models (LLMs) on customized tasks. This pioneering research introduces the Automatic Prompt Engineering Toolbox (APET), which enables GPT-4 to autonomously apply prompt engineering techniques. By leveraging sophisticated strategies such as Expert Prompting, Chain of Thought, and Tree of Thoughts, APET empowers GPT-4 to dynamically optimize prompts, resulting in substantial improvements in tasks like Word Sorting (4.4% increase) and Geometric Shapes (6.8% increase). Despite encountering challenges in complex tasks such as Checkmate in One (-14.8%), these findings demonstrate the transformativ

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  10. Prompt Engineering Large Language Models' Forecasting Capabilities

    Philipp Schoenegger, Cameron R. Jones, Philip E. Tetlock, et al. · 2025 · arXiv (Cornell University)

    Large language model performance can be improved in a large number of ways. Many such techniques, like fine-tuning or advanced tool usage, are time-intensive and expensive. Although prompt engineering is significantly cheaper and often works for simpler tasks, it remains unclear whether prompt engineering suffices for more complex domains like forecasting. Here we show that small prompt modifications rarely boost forecasting accuracy beyond a minimal baseline. In our first study, we tested 38 prompts across Claude 3.5 Sonnet, Claude 3.5 Haiku, GPT-4o, and Llama 3.1 405B. In our second, we introduced compound prompts and prompts from external sources, also including the reasoning models o1 an

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  11. Prompt Engineering Large Language Models for Healthcare Decision-Making Training

    Olena Bolgova, Paul K. Ganguly, Volodymyr Mavrych · 2025 · International Conference on Decision Aid Sciences and Applications (DASA)

    Background: Traditional grading of medical short-answer questions is resource-intensive and potentially inconsistent, limiting educational feedback quality. Large language models (LLMs) offer potential solutions, but their effectiveness depends heavily on the design of the prompting strategy.Objective: To evaluate the impact of progressive prompting strategies on DeepSeek R1's performance in grading medical short-answer questions compared to expert human graders.Methods: We assessed DeepSeek's grading accuracy across four prompting strategies: zero-shot (P1), few-shot with examples (P2), chain-of-thought reasoning (P3), and advanced prompting with expert criteria (P4). Three expert faculty m

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  12. Large Language Models and Prompt Engineering

    Mohammad Amin Kuhail, Mohammad Tubishat, Sarah Kohail, et al. · 2026 · Chapman & Hall/CRC eBooks

    This book provides a structured and accessible introduction to the theory, design, and real-world use of large language models (LLMs). As generative AI systems become embedded across industries, this book offers readers a balanced perspective that combines technical foundations with practical guidance and critical reflection. Beginning with the fundamentals of LLMs and prompt engineering, this book progressively explores advanced prompting techniques, application and interaction frameworks, and the underlying architectures that power modern language models. Dedicated chapters examine how LLMs are being applied in high-impact domains such as healthcare, customer support, and education, highli

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