Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective

• URL: http://arxiv.org/abs/2508.01443v1
• Date: Sat, 02 Aug 2025 17:11:40 GMT
• Title: Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
• Authors: Jingzhi Gong, Rafail Giavrimis, Paul Brookes, Vardan Voskanyan, Fan Wu, Mari Ashiga, Matthew Truscott, Mike Basios, Leslie Kanthan, Jie Xu, Zheng Wang,
• Abstract summary: We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates task-specific prompts across diverse large language models (LLMs)<n>MPCO seamlessly deploys on the ARTEMIS industrial platform for automated validation and scaling.<n>Analysis shows that 96% of the top-performing optimizations stem from meaningful edits.
• Score: 7.699689169768917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a growing interest in leveraging large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM optimization systems in production environments. To address this, we introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages meta-prompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts, and it seamlessly deploys on the ARTEMIS industrial platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting, and that all three major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

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