Prompt Engineering for Generative AI: Techniques, Applications, Challenges, and Future Directions
- 1R C Patel college of polytechnic and engineering
Abstract
Generative Artificial Intelligence (Generative AI) has transformed the way computers generate text, images, code, and other forms of content. Prompt engineering is the process of designing and optimizing instructions to guide generative AI models toward relevant, useful, and accurate outputs. This paper reviews major prompt engineering techniques, including zero-shot prompting, few-shot prompting, role-based prompting, chain-of-thought prompting, structured prompting, automatic prompt optimization, and retrieval-augmented generation.
The paper discusses applications of prompt engineering in education, software development, healthcare information systems, and business automation. It also examines key challenges, including hallucinations, ambiguity, bias, security vulnerabilities, privacy concerns, reproducibility, and evaluation difficulties. A comparative framework is presented to examine the strengths, limitations, and potential use cases of different prompting techniques. Finally, future research directions are discussed, including automated prompt optimization, multimodal prompting, reliable evaluation methods, and responsible AI practices.
This work aims to provide a structured overview of prompt engineering and its role in improving the usability, reliability, and effectiveness of generative AI systems.
Keywords: Prompt Engineering, Generative AI, Large Language Models, Natural Language Processing, Few-Shot Learning, Retrieval-Augmented Generation, Responsible AI.
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BibTeX
@misc{mali2026prompt,
title = {{Prompt Engineering for Generative AI: Techniques, Applications, Challenges, and Future Directions}},
author = {Shweta Mali},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23529416},
url = {https://zenodo.org/records/23529416},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Prompt Engineering for Generative AI: Techniques, Applications, Challenges, and Future Directions
AU - Shweta Mali
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23529416
UR - https://zenodo.org/records/23529416
AB - Generative Artificial Intelligence (Generative AI) has transformed the way computers generate text, images, code, and other forms of content. Prompt engineering is the process of designing and optimizing instructions to guide generative AI models toward relevant, useful, and accurate outputs. This paper reviews major prompt engineering techniques, including zero-shot prompting, few-shot prompting, role-based prompting, chain-of-thought prompting, structured prompting, automatic prompt optimization, and retrieval-augmented generation. The paper discusses applications of prompt engineering in education, software development, healthcare information systems, and business automation. It also examines key challenges, including hallucinations, ambiguity, bias, security vulnerabilities, privacy concerns, reproducibility, and evaluation difficulties. A comparative framework is presented to examine the strengths, limitations, and potential use cases of different prompting techniques. Finally, future research directions are discussed, including automated prompt optimization, multimodal prompting, reliable evaluation methods, and responsible AI practices. This work aims to provide a structured overview of prompt engineering and its role in improving the usability, reliability, and effectiveness of generative AI systems. Keywords: Prompt Engineering, Generative AI, Large Language Models, Natural Language Processing, Few-Shot Learning, Retrieval-Augmented Generation, Responsible AI.
ER -