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arXiv · 2507.14241

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models

Rithesh Murthy·Ming Zhu·Liangwei Yang·Jielin Qiu·Juntao Tan·Shelby Heinecke·Caiming Xiong·Silvio Savarese·Huan Wang
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Abstract

Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts without requiring manual tuning or domain expertise. Promptomatix supports both a lightweight meta-prompt-based optimizer and a DSPy-powered compiler, with modular design enabling future extension to more advanced frameworks. The system analyzes user intent, generates synthetic training data, selects prompting strategies, and refines prompts using cost-aware objectives. Evaluated across 5 task categories, Promptomatix achieves competitive or superior performance compared to existing libraries, while reducing prompt length and computational overhead making prompt optimization scalable and efficient.

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Rithesh Murthy, Ming Zhu, Liangwei Yang, Jielin Qiu, Juntao Tan, Shelby Heinecke, Caiming Xiong, Silvio Savarese, Huan Wang. 2025-07-17. Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models. https://arxiv.org/abs/2507.14241

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