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Research papers on Prompt engineering

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  1. A Survey of Large Language Models

    Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. · 2023 · ArXiv · 4,706 citations

    The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including supervi

  2. BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence

    Nowickij (Navitski), Kirill Vladimirovich · 2022 · arXiv (Cornell University) · 4,277 citations

    AbstractThere is a failure mode in large language models that we do not have a good name for, and thatwe therefore tend not to treat seriously enough. It is not hallucination — the model is not assertingsomething false. It is not refusal — the model answers at length. It is the production of responses thatcarry the complete outward form of careful reasoning while the cognitive work that reasoning issupposed to represent has not, in any meaningful sense, occurred. We call this theatrical compliance,and we argue that it is, in practical terms, more dangerous than either of the failure modes thatcurrently dominate alignment research. This paper identifies the phenomenon, characterizes its fivep

  3. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al. · 2022 · ACM Computing Surveys · 3,665 citations

    This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P ( y|x ), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the original input x is modified using a template into a textual string prompt x′ that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string x̂ , from which the final output y can be derived. This framework is powe

  5. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

    Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, et al. · 2024 · ArXiv · 948 citations

    Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on the given prompt. Prompts can be natural language instructions that provide context to guide the model or learned vector representations that activate relevant knowledge. This burgeoning field has enabled success across vario

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

    Yan Hu, Iqra Ameer, X. Zuo, et al. · 2024 · Journal of the American Medical Informatics Association : JAMIA · 360 citations

    Abstract 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)

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

    B.‐C. CHEN, Zhaofeng Zhang, Nicolas Langrené, et al. · 2025 · Patterns · 273 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

  9. Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models

    Hendrik Strobelt, Albert Webson, Victor Sanh, et al. · 2022 · IEEE Transactions on Visualization and Computer Graphics · 191 citations

    State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training. This approach has gained popularity in recent years, and researchers have demonstrated prompts that achieve strong accuracy on specific NLP tasks. However, finding a prompt for new tasks requires experimentation. Different prompt templates with different wording choices lead to significant accuracy differences. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts. We developed a workflow that allows users to first focus on model feedback using small data before moving

  11. A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks

    Shubham Vatsal, Harsh Dubey · 2024 · ArXiv · 115 citations

    Large language models (LLMs) have shown remarkable performance on many different Natural Language Processing (NLP) tasks. Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant performance gains on various NLP tasks. Prompt engineering requires composing natural language instructions called prompts to elicit knowledge from LLMs in a structured way. Unlike previous state-of-the-art (SoTA) models, prompt engineering does not require extensive parameter re-training or fine-tuning based on the given NLP task and thus solely operates on the embedded knowledge of LLMs. Additionally, LLM enthusiasts can intelligently extract LLMs' knowled

  12. Fine-tuning and prompt engineering for large language models-based code review automation

    Chanathip Pornprasit, C. Tantithamthavorn · 2024 · Inf. Softw. Technol. · 107 citations

    Context: The rapid evolution of Large Language Models (LLMs) has sparked significant interest in leveraging their capabilities for automating code review processes. Prior studies often focus on developing LLMs for code review automation, yet require expensive resources, which is infeasible for organizations with limited budgets and resources. Thus, fine-tuning and prompt engineering are the two common approaches to leveraging LLMs for code review automation. Objective: We aim to investigate the performance of LLMs-based code review automation based on two contexts, i.e., when LLMs are leveraged by fine-tuning and prompting. Fine-tuning involves training the model on a specific code review da

  13. Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models

    F. Trad, Ali Chehab · 2024 · Mach. Learn. Knowl. Extr. · 105 citations

    Large Language Models (LLMs) are reshaping the landscape of Machine Learning (ML) application development. The emergence of versatile LLMs capable of undertaking a wide array of tasks has reduced the necessity for intensive human involvement in training and maintaining ML models. Despite these advancements, a pivotal question emerges: can these generalized models negate the need for task-specific models? This study addresses this question by comparing the effectiveness of LLMs in detecting phishing URLs when utilized with prompt-engineering techniques versus when fine-tuned. Notably, we explore multiple prompt-engineering strategies for phishing URL detection and apply them to two chat model

  14. The Promises and Pitfalls of Large Language Models as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback

    L. Jacobsen, Kira Elena Weber · 2025 · AI · 59 citations

    Background/Objectives: Artificial intelligence (AI) is transforming higher education (HE), reshaping teaching, learning, and feedback processes. Feedback generated by large language models (LLMs) has shown potential for enhancing student learning outcomes. However, few empirical studies have directly compared the quality of LLM feedback with feedback from novices and experts. This study investigates (1) the types of prompts needed to ensure high-quality LLM feedback in teacher education and (2) how feedback from novices, experts, and LLMs compares in terms of quality. Methods: To address these questions, we developed a theory-driven manual to evaluate prompt quality and designed three prompt

  15. Prompt Engineering for Large Language Models in Interventional Radiology.

    Nicholas Dietrich, Nicholas C Bradbury, Christopher T. Loh · 2025 · AJR. American journal of roentgenology · 40 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, demonstrating their application in IR-specific contexts. Practical examples illustrate how these techniques can be effectively structured for workplace and clinical use. Addit

  16. Optimizing Large Language Models: A Deep Dive into Effective Prompt Engineering Techniques

    Minjun Son, Yun-Jae Won, Sungjin Lee · 2025 · Applied Sciences · 26 citations

    Recent advancements in Natural Language Processing (NLP) technologies have been driven at an unprecedented pace by the development of Large Language Models (LLMs). However, challenges remain, such as generating responses that are misaligned with the intent of the question or producing incorrect answers. This paper analyzes various Prompt Engineering techniques for large-scale language models and identifies methods that can optimize response performance across different datasets without the need for extensive retraining or fine-tuning. In particular, we examine prominent Prompt Engineering techniques including In-Context Learning (ICL), Chain of Thought (CoT), Retrieval-Augmented Generation (

  17. Leveraging Prompt Engineering in Large Language Models for Accelerating Chemical Research

    Feifei Luo, Jinglang Zhang, Qilong Wang, et al. · 2025 · ACS Central Science · 26 citations

    Artificial intelligence (AI) using large language models (LLMs) such as GPTs has revolutionized various fields. Recently, LLMs have also made inroads in chemical research even for users without expertise in coding. However, applying LLMs directly may lead to “hallucinations”, where the model generates unreliable or inaccurate information and is further exacerbated by limited data set and inherent complexity of chemical reports. To counteract this, researchers have suggested prompt engineering, which can convey human ideas formatively and unambiguously to LLMs and simultaneously improve LLMs’ reasoning capability. So far, prompt engineering remains underutilized in chemistry, with many chemis

  18. Optimizing Large Language Models in Radiology and Mitigating Pitfalls: Prompt Engineering and Fine-tuning.

    T. Kim, Michael Makutonin, Reza Sirous, et al. · 2025 · Radiographics : a review publication of the Radiological Society of North America, Inc · 26 citations

    Large language models (LLMs) such as generative pretrained transformers (GPTs) have had a major impact on society, and there is increasing interest in using these models for applications in medicine and radiology. This article presents techniques to optimize these models and describes their known challenges and limitations. Specifically, the authors explore how to best craft natural language prompts, a process known as prompt engineering, for these models to elicit more accurate and desirable responses. The authors also explain how fine-tuning is conducted, in which a more general model, such as GPT-4, is further trained on a more specific use case, such as summarizing clinical notes, to fur

  19. Prompt engineering for accurate statistical reasoning with large language models in medical research

    S. Vilakati · 2025 · Frontiers in Artificial Intelligence · 21 citations

    Background The integration of generative artificial intelligence (AI), particularly large language models (LLMs), into medical statistics offers transformative potential. However, it also introduces risks of erroneous responses, especially in tasks requiring statistical rigor. Objective To evaluate the effectiveness of various prompt engineering strategies in guiding LLMs toward accurate and interpretable statistical reasoning in biomedical research. Methods Four prompting strategies: zero-shot, explicit instruction, chain-of-thought, and hybrid were assessed using artificial datasets involving descriptive and inferential statistical tasks. Outputs from GPT-4.1 and Claude 3.7 Sonnet were eva

  20. A comprehensive taxonomy of prompt engineering techniques for large language models

    Yao-Yang Liu, Zheng Zheng, Feng Zhang, et al. · 2025 · Frontiers of Computer Science · 21 citations

    Large Language Models (LLMs) have demonstrated remarkable performance across various downstream tasks, as evidenced by numerous studies. Since 2022, generative AI has shown significant potential in diverse application domains, including gaming, film and television, media, and finance. By 2023, the global AI-generated content (AIGC) industry had attracted over $26 billion in investment. As LLMs become increasingly prevalent, prompt engineering has emerged as a key research area to enhance user-AI interactions and improve LLM performance. The prompt, which serves as the input instruction for the LLM, is closely linked to the model’s responses. Prompt engineering refines the content and structu

  21. Mitigating spatial hallucination in large language models for path planning via prompt engineering

    Hongjie Zhang, Hourui Deng, Jie Ou, et al. · 2025 · Scientific Reports · 20 citations

    Spatial reasoning in Large Language Models (LLMs) serves as a foundation for embodied intelligence. However, even in simple maze environments, LLMs often struggle to plan correct paths due to hallucination issues. To address this, we propose S2ERS, an LLM-based technique that integrates entity and relation extraction with the on-policy reinforcement learning algorithm Sarsa for optimal path planning. We introduce three key improvements: (1) To tackle the hallucination of spatial, we extract a graph structure of entities and relations from the text-based maze description, aiding LLMs in accurately comprehending spatial relationships. (2) To prevent LLMs from getting trapped in dead ends due t

  22. Realizing the possibilities of the large language models: Strategies for prompt engineering in educational inquiries

    Ana-Paula Correia, Sean Hickey, Fan Xu · 2025 · Theory Into Practice · 19 citations

    ABSTRACT This article examines the potential of Large Language Models (LLMs) to transform educational inquiries, emphasizing effective prompt engineering strategies. It begins by introducing LLMs and applications of generative AI tools in education. The article explores the fundamental principles of prompt engineering and LLMs, outlining strategies for educators to interact effectively with these models. It emphasizes the vital role of teacher-LLM interactions, highlighting the need for carefully crafted prompts to elicit high quality and pedagogically valuable responses. We discuss chain-of-thought prompting as a method for promoting deeper reasoning in LLM outputs. We offer other practical

  23. Benchmarking 21 Open-Source Large Language Models for Phishing Link Detection with Prompt Engineering

    A. Nasution, Winda Monika, Aytuğ Onan, et al. · 2025 · Inf. · 15 citations

    Phishing URL detection is critical due to the severe cybersecurity threats posed by phishing attacks. While traditional methods rely heavily on handcrafted features and supervised machine learning, recent advances in large language models (LLMs) provide promising alternatives. This paper presents a comprehensive benchmarking study of 21 state-of-the-art open-source LLMs—including Llama3, Gemma, Qwen, Phi, DeepSeek, and Mistral—for phishing URL detection. We evaluate four key prompt engineering techniques—zero-shot, role-playing, chain-of-thought, and few-shot prompting—using a balanced, publicly available phishing URL dataset, with no fine-tuning or additional training of the models conducte

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