ISSN2320-2882
Impact Factor9.17
UGC ParametersAligned
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CrossRefDOI
ESTD2013
Peer ReviewTransparent
AccessOpen
Submission: Always Open
Paper Title
Evaluating the Impact of Prompt Design on Large Language Model Performance
Authors
Lavanya bai R , Shri Preetha S
Keywords
- Large Language Models, Prompt Engineering, Performance Evaluation, Chain-of-Thought, Benchmarking
Abstract
Large Language Models (LLMs) have become central to modern artificial intelligence applications, yet their effectiveness strongly depends on how user instructions are formulated. Prompt engineering has emerged as a practical technique to guide model behavior without modifying model parameters or performing additional training. This paper presents an empirical benchmarking study of widely used prompt engineering strategies across multiple large language models, including both proprietary and open-source architectures. The study examines zero-shot, few-shot, chain-of-thought, role-based, and structured prompting techniques using diverse evaluation tasks such as logical reasoning, text summarization, and code generation. Model performance is evaluated based on accuracy, consistency of responses, and computational latency. Experimental results demonstrate that prompt engineering consistently improves task performance across all models, with chain-of-thought and structured prompts showing the most notable gains. Additionally, smaller and open-source models benefit more significantly from optimized prompts when compared to advanced models with strong baseline capabilities. The findings confirm that prompt engineering is a low-cost, model-agnostic approach for enhancing LLM performance and provide practical guidance for selecting effective prompting strategies in real-world deployments.
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IJCRT — journal style
"Evaluating the Impact of Prompt Design on Large Language Model Performance", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.13, Issue 12, page no.i55-i62, December 2025, Available :https://ijcrt.org/papers/IJCRT2512918.pdf
APA — 7th edition
R, L. B., & S, P. (2025). Evaluating the Impact of Prompt Design on Large Language Model Performance. International Journal of Creative Research Thoughts, 13(12), i55-i62. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918
MLA — 9th edition
R, Lavanya bai, and Preetha S. "Evaluating the Impact of Prompt Design on Large Language Model Performance." International Journal of Creative Research Thoughts, vol. 13, no. 12, 2025, pp. i55-i62, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918.
Chicago — 17th, bibliography
R, Lavanya bai, and Preetha S. "Evaluating the Impact of Prompt Design on Large Language Model Performance." International Journal of Creative Research Thoughts 13, no. 12 (2025): i55-i62. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918.
Harvard — author–date
R, L.B. and S, P. (2025) 'Evaluating the Impact of Prompt Design on Large Language Model Performance', International Journal of Creative Research Thoughts, 13(12), pp. i55-i62. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918
IEEE — numbered reference
L. B. R and P. S, "Evaluating the Impact of Prompt Design on Large Language Model Performance," IJCRT, vol. 13, no. 12, pp. i55-i62, Dec. 2025.
Vancouver — biomedical
R LB, S P. Evaluating the Impact of Prompt Design on Large Language Model Performance. IJCRT. 2025 Dec;13(12):i55-i62.
AMA — 11th edition
R LB, S P. Evaluating the Impact of Prompt Design on Large Language Model Performance. IJCRT. 2025;13(12):i55-i62. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2512918,
author = {Lavanya bai R and Preetha S},
title = {Evaluating the Impact of Prompt Design on Large Language Model Performance},
journal = {International Journal of Creative Research Thoughts},
volume = {13},
number = {12},
pages = {i55-i62},
year = {2025},
month = {December},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - R, Lavanya bai
AU - S, Preetha
TI - Evaluating the Impact of Prompt Design on Large Language Model Performance
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 13
IS - 12
PY - 2025
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918
SP - i55-i62
ER -
Issue
Volume 13 Issue 12,
December-2025
Pages : i55-i62
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 13, Issue 12, December 2025
- Pages
- i55-i62
- Published
- December 2025
- Registration ID
- IJCRT_299567
- Published Paper ID
- IJCRT2512918
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Chennai, Tamilnadu, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 185
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT2512918
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT2512918.pdf
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