Authors : Khalid Al Thinyan; Mohammad Al Wohaibi; Abdullah Al Shehri

Volume/Issue : Volume 10 - 2025, Issue 4 - April

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DOI : https://doi.org/10.38124/ijisrt/25apr602

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Abstract : Prompt engineering represents a systematic, data-centric approach that significantly enhances the design and optimization of prompts for language models. This methodology leverages analytical frameworks to assess and refine prompts rigorously, ultimately driving improved educational outcomes. Effective, prompt engineering involves articulating precise inquiries that elicit optimal responses from language models, which is fundamental to its transformative potential. This article comprehensively examines prompt engineering, highlighting its ability to revolutionize language modeling [1]. It delves into practical methodologies employed in various real-world contexts and outlines best practices, fostering an optimistic outlook on the future capabilities of this approach.

Keywords : Prompt Engineering, Large Language Models, Natural Language Processing

References :

1. Prompt Engineering Guide
2. Prompt Engineering: Revolutionizing Problem-Solving in Engineering
3. Eight Prompt Engineering Implementations
4. Prompt Engineering: The Guide to Mastering the Art of Talking to AI
5. 12 Prompt Engineering Techniques
6. What is Prompt Engineering?
7. Prompt Engineering Best Practices: Tips, Tricks, and Tools
8. What is Prompt Engineering? A Detailed Guide For 2024
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10. Large Language Models Are Unreliable Judges Page - Jonathan H. Choi, University of Southern California, University of Southern California Gould School of Law
11. Deep Learning Concepts in Operations Research - dited ByBiswadip Basu Mallik, Gunjan Mukherjee, Rahul Kar, Aryan Chaudhary -Chapter 17
12. Optimizing Prompt Engineering for Improved Generative AI Content - Author: Pablo Ortolan (Universidad Pontificia)
13. Prompt Engineering Importance and Applicability with Generative AI, Journal of Computer and Communications, Author: Prashant Bansal
14. A Deep Dive in to Neural Models in NLP, International Journal of Engineering Research & Technology (IJERT), Authors: Aisheek Mazumder, Kumar Sanu, Ayush Kumar, Prabhat Kumar, Aryan Chauhan, Er. Simran Kaur Birdi, Paper ID: IJERTV13IS100133, Volume & Issue: Volume 13, Issue 10 (October 2024), Published (First Online): 07-11-2024