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Related Experiment Videos

Green prompt engineering for sustainable generative AI.

Sanjay Podder¹, Hema Date¹, Shankar Murthy¹

¹Indian Institute of Management Mumbai, Powai, Mumbai, Maharashtra, 400087, India.

Environmental Science and Ecotechnology

|March 30, 2026

Summary

Optimizing prompts for large language models (LLMs) reduces energy consumption and carbon footprint. Applying these best practices cut energy use by 32-48% in generative AI applications.

Keywords:

Generative AI Green AI Green computing Greenhouse gases Inference mechanisms Prompt engineering

Related Experiment Videos

Area of Science:

Artificial Intelligence
Computer Science

Background:

Prompt engineering is crucial for controlling large language model (LLM) outputs.
High computational costs and energy consumption are significant challenges in generative AI.
Suboptimal prompts increase LLM inference iterations, escalating energy use and carbon footprint.

Purpose of the Study:

To propose practices and guidelines for energy-efficient prompt design.
To minimize reiterations required for desired LLM responses.
To enhance the sustainability of generative AI applications.

Main Methods:

Developed a series of best practices for prompt engineering.
Conducted empirical evaluations across various LLMs and scenarios.
Measured energy consumption and greenhouse gas emissions.

Main Results:

Applied best practices reduced energy consumption by 32-48%.
Significant decrease in operational greenhouse gas emissions observed.
Validated effectiveness across diverse LLMs and test cases.

Conclusions:

Proposed best practices enhance LLM prompt inferencing energy efficiency.
These guidelines can be integrated into generative AI design frameworks.
Advocates for sustainable and effective deployment of generative AI.