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Research papers on Prompt engineering

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  3. 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

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  4. 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

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  5. 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

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  6. 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

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  7. 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.

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