Generative Artificial Intelligence and Prompt Engineering: A Comprehensive Guide to Models, Methods, and Best Practices

Volume 10, Issue 2, Page No 01-11, 2025

Author’s Name: Maikel Leon

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Department of Business Technology, Miami Herbert Business School, University of Miami, Miami, Florida, USA

^a)whom correspondence should be addressed. E-mail: mleon@miami.edu

Adv. Sci. Technol. Eng. Syst. J. 10(2), 01-11 (2025); DOI: 10.25046/aj100201

Keywords: Generative AI, Large Language Models, Prompt Engineering

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Abstract

This article enhances discussions on Generative Artificial Intelligence (GenAI) and prompt engineering by exploring critical pitfalls and industry-specific advantages. It begins with a foundational overview of AI evolution, emphasizing how generative models such as GANs, VAEs, and Transformers have revolutionized language processing, image generation, and drug discovery. Prompt engineering is highlighted as a key methodology for directing model outputs with precision and ethical awareness, enabling applications in Natural Language Processing (NLP), content personalization, and decision support. The revised sections detail how prompt engineering can be misapplied, underscoring common errors like overly restrictive or ambiguous prompts that compromise GenAI’s accuracy, ethicality, and creative capacity. Equally, the paper showcases high-impact use cases in finance, education, healthcare, and beyond, illustrating how carefully formulated prompts can strengthen risk detection, enhance student learning, improve clinical decision-making, and foster product innovation. The expanded discussion of industry alignment illustrates the tangible value these techniques offer across diverse sectors, ultimately reinforcing the notion that prompt engineering is central to maximizing GenAI’s transformative potential. Future directions address emerging trends, from multimodal fusion and domain-specific fine-tuning to adaptive prompt designs that leverage real-time user feedback, further solidifying the role of responsible prompt engineering in shaping the next generation of intelligent and ethically aligned AI solutions.

Received: 06 January 2025 Revised: 12 February 2025 Accepted: 14 February 2025 Online: 07 March 2025

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Generative Artificial Intelligence and Prompt Engineering: A Comprehensive Guide to Models, Methods, and Best Practices