Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting
As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideration. This motivates evaluating LLM-generated code beyond functional correctness by considering execution efficiency and energy consumption. However, despite substantial advances in code generation, frontier LLMs are rarely evaluated based on the energy efficiency of the code they produce. In this work, we conduct a comprehensive evaluation of 21 prompting strategies for energy-efficient code generation and identify 8 strategies for evaluation across 10 widely used open-weight and proprietary LLMs. We evaluate their effectiveness for both Pytho...
| Authors | Ritika Rekhi, Bing Zhang, Md Arman Islam et al. |
|---|---|
| Primary category | cs.SE |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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