# Prompt engineering and its implications on the energy consumption of Large Language Models

arXiv: https://arxiv.org/abs/2501.05899 · HTML: https://mlsanity.com/paper/2501.05899/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks](https://mlsanity.com/paper/2506.20009/) (2025)
- [Mapping the Landscape of Artificial Intelligence in Life Cycle Assessment Using Large Language Models](https://mlsanity.com/paper/2602.22500/) (2026)
- [Developing LLM-based Multi-Agent Systems in Software Engineering: A Mixed-Method Experience Report](https://mlsanity.com/paper/2608.11965/) (2026)
- [PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses](https://mlsanity.com/paper/2603.10477/) (2026)
- [From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks](https://mlsanity.com/paper/2608.12350/) (2026)

## Abstract (arXiv)

Reducing the environmental impact of AI-based software systems has become critical. The intensive use of large language models (LLMs) in software engineering poses severe challenges regarding computational resources, data centers, and carbon emissions. In this paper, we investigate how prompt engineering techniques (PETs) can impact the carbon emission of the Llama 3 model for the code generation task. We experimented with the CodeXGLUE benchmark to evaluate both energy consumption and the accuracy of the generated code using an isolated testing environment. Our initial results show that the energy consumption of LLMs can be reduced by using specific tags that distinguish different prompt parts. Even though a more in-depth evaluation is needed to confirm our findings, this work suggests that prompt engineering can reduce LLMs' energy consumption during the inference phase without compromising performance, paving the way for further investigations.