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

arXiv: https://arxiv.org/abs/2508.01443 · HTML: https://mlsanity.com/paper/2508.01443/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 74 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 24 of 24)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization](https://mlsanity.com/paper/2310.16427/) (2023)
- [AIDE: AI-Driven Exploration in the Space of Code](https://mlsanity.com/paper/2502.13138/) (2025)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding](https://mlsanity.com/paper/2401.12954/) (2024)
- [Learning Performance-Improving Code Edits](https://mlsanity.com/paper/2302.07867/) (2023)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [Explainable Automated Debugging via Large Language Model-driven Scientific Debugging](https://mlsanity.com/paper/2304.02195/) (2023)
- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI](https://mlsanity.com/paper/2411.18241/) (2024)
- [EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization](https://mlsanity.com/paper/2405.15189/) (2024)
- [A Comparative Analysis of Large Language Models for Code Documentation Generation](https://mlsanity.com/paper/2312.10349/) (2023)
- [MetaPrompting: Learning to Learn Better Prompts](https://mlsanity.com/paper/2209.11486/) (2022)
- [LangProp: A code optimization framework using Large Language Models applied to driving](https://mlsanity.com/paper/2401.10314/) (2024)
- [Meta Prompting for AI Systems](https://mlsanity.com/paper/2311.11482/) (2023)
- [RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents](https://mlsanity.com/paper/2406.11132/) (2024)
- [Language Models for Code Optimization: Survey, Challenges and Future Directions](https://mlsanity.com/paper/2501.01277/) (2025)
- [Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks](https://mlsanity.com/paper/2310.10362/) (2023)

## Built on by (most cited first, 7 of 7)

- [Evolving Excellence: Automated Optimization of LLM-based Agents](https://mlsanity.com/paper/2512.09108/) (2025)
- [Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests](https://mlsanity.com/paper/2601.04886/) (2026)
- [GA4GC: Greener Agent for Greener Code via Multi-Objective Configuration Optimization](https://mlsanity.com/paper/2510.04135/) (2025)
- [FasterPy: An LLM-based Code Execution Efficiency Optimization Framework](https://mlsanity.com/paper/2512.22827/) (2025)
- [Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search](https://mlsanity.com/paper/2605.20244/) (2026)
- [OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms](https://mlsanity.com/paper/2604.26211/) (2026)
- [COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation](https://mlsanity.com/paper/2608.04336/) (2026)

## Abstract (arXiv)

There is a growing interest in leveraging multiple 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 systems in production environments. 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 metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization 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 major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.