Cost, Complexity, and Efficacy of Prompt Engineering Techniques for Large Language Models

Author(s)	Milind Cherukuri
Country	United States
Abstract	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 cost difference between manual/few-shot and zero-shot methods. Complexity analysis reveals that more intricate prompts often lead to convoluted outputs, challenging human review and implementation. Our findings guide the selection of prompt engineering strategies to optimize both performance and resource utilization
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-29
Cite This	Cost, Complexity, and Efficacy of Prompt Engineering Techniques for Large Language Models - Milind Cherukuri - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.2584
DOI	https://doi.org/10.71097/IJSAT.v16.i2.2584
Short DOI	https://doi.org/g9g7z7

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology