FolioStart free

Computer Science · Literature

Research papers on Prompt engineering

Recent and highly-cited academic work on prompt engineering, gathered from Semantic Scholar, CrossRef and OpenAlex.

Search all 200M+ papers on this topic, free →Or track new prompt engineering papers automatically as they publish

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

    B.‐C. CHEN, Zhaofeng Zhang, Nicolas Langrené, et al. · 2025 · Patterns · 500 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

    Save this paper

  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 · 306 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

    Save this paper

  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

    Save this paper

  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

    Save this paper

  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

    Save this paper

  12. Cost, Complexity, and Efficacy of Prompt Engineering Techniques for Large Language Models

    Milind Cherukuri - · 2025 · International Journal on Science and Technology · 1 citations

    This research investigates the impact of various prompt engineering techniques on the length, cost, complexity, and accuracy of responses from large language models (LLMs). By comparing direct prompting with zero-shot, few-shot, and chain-of-thought (CoT) methods on tasks like GSM8K and creative writing, I analyze the trade-offs between token usage and response quality. Results show that while zero-shot CoT prompting is highly effective and cost-efficient, other methods like Least-to-Most and Tree-of-Thought add significant length and complexity without proportional accuracy gains. Additionally, I discuss the financial implications, finding that GPT-4’s unique pricing structure narrows the c

    Save this paper

  13. 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

    Save this paper

  14. 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

    Save this paper

  15. 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

    Save this paper

  16. Prompt engineering for large language models in test case generation

    Anatolii Husakovskyi · 2026 · Information Technology and Computer Engineering

    The purpose of this study was to perform a three-scenario comparative analysis of the accuracy of intelligent fault diagnosis in complex technical systems using ship power plants (SPPs) as a representative case. The research sought to determine which diagnostic configuration ensures the highest accuracy and robustness under varying operational conditions. Three methodological configurations were analysed: a baseline model based on Case-Based Reasoning (CBR); CBR enhanced with probabilistic analysis using Bayesian networks and Markov chains; and a comprehensive integration of CBR with probabilistic models and simulation modelling of cascading failures. Experiments were conducted under three t

    Save this paper

  17. Prompt Engineering and Optimization in Large Language Models

    Ranjith Gundam, Kalyan Mandadhi · 2026 · International Scientific Journal of Engineering and Management

    Abstract: Large Language Models (LLMs) have emerged as transformative technologies, enabling sophisticated natural language understanding and generation across diverse applications. However, their effectiveness is heavily dependent on how instructions and queries are formulated. Prompt engineering—the art and science of crafting optimal input instructions—has become critical to unlocking the full potential of these models. This systematic review investigates contemporary approaches to prompt engineering and optimization in LLMs, examining techniques such as few-shot learning, chain-of-thought prompting, retrieval augmented generation, and dynamic prompt optimization. The study analyzes vario

    Save this paper

  18. Prompt Engineering in Large Language Models: A Systematic Survey of Optimization Techniques and Real-World Applications

    Susmith Barigidad · 2025 · Nanotechnology Perceptions

    Consequently, over the past decade, prompt engineering as a transformal technique has emerged to optimally facilitate large language models (LLMs) and vision language models (VLMs) for their transferable usage in various fields without the requirement of model retraining. The focus of this paper is on a thorough analysis of the optimization techniques available in prompt engineering field such as Zero Shot, Few Shot prompting, Chain of Thought, Auto CoT, Logical CoT (LogiCoT) and Retrieval augmented generation (RAG). These techniques boost the performance of LLMs on real-world use cases such as natural language understanding, commonsense reasoning and general solving sophisticated problems a

    Save this paper

  19. Beyond Prompt Engineering: The Evolution of Reasoning in Advanced Large Language Models

    Nan Wu - · 2025 · International Journal on Science and Technology

    This paper explores the evolving role of prompt engineering as large language models (LLMs) develop enhanced intrinsic reasoning capabilities. Initially essential for effective model performance, explicit prompting techniques are becoming less crucial with advanced models like GPT-4.5 and DeepSeek R1. Benchmark analyses indicate that intrinsic reasoning now solves most reasoning tasks efficiently, though explicit prompting still provides incremental benefits in specialized scenarios. Future directions emphasize intrinsic reasoning improvements, automated prompting strategies, and refined evaluation methods, marking a fundamental shift in leveraging LLMs.

    Save this paper

Write your paper with these sources

Folio is the integrity-first research workspace: search 200M+ papers, save sources, and write with citations that format themselves. Free for students and researchers.

Start writing free →