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Towards Sustainable Inference of LLMs for Medical Education Through Token Count Minimization.

Hyunggu Jung^1,2, Jiyoo Min³, Yunseo Moon³

¹College of Nursing, Seoul National University, Seoul, Republic of Korea.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

Reducing large language model (LLM) carbon emissions is crucial. Our method minimizes input prompt tokens for medical students, significantly cutting costs and improving efficiency.

Keywords:

Generative AI Medical Education Prompt Engineering Sustainability

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Area of Science:

Artificial Intelligence
Natural Language Processing
Medical Education

Background:

Growing concern over carbon emissions from large language model (LLM) inference.
Need for efficient prompt engineering in specialized fields like medical education.
Current prompt lengths can be resource-intensive.

Purpose of the Study:

To develop a method for minimizing input prompt token counts for LLMs.
To reduce the environmental impact of LLM usage in medical education.
To enhance the efficiency of LLM interactions for medical students.

Main Methods:

Utilized English physical examination course materials.
Applied translation and paraphrasing techniques to prompts.
Compared token counts of baseline and optimized prompts.

Main Results:

English baseline prompts were found to have fewer tokens than Korean prompts.
Proposed paraphrased prompts significantly reduced token counts compared to baseline prompts.
Demonstrated a method for token optimization in LLM prompts.

Conclusions:

Prompt engineering can effectively reduce the carbon footprint of LLMs.
Paraphrasing is a viable strategy for creating token-efficient prompts.
Optimized prompts benefit both environmental sustainability and user efficiency in medical education.