Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912500052656128 |
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| author | Murthy, Rithesh Zhu, Ming Yang, Liangwei Qiu, Jielin Tan, Juntao Heinecke, Shelby Xiong, Caiming Savarese, Silvio Wang, Huan |
| author_facet | Murthy, Rithesh Zhu, Ming Yang, Liangwei Qiu, Jielin Tan, Juntao Heinecke, Shelby Xiong, Caiming Savarese, Silvio Wang, Huan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models Murthy, Rithesh Zhu, Ming Yang, Liangwei Qiu, Jielin Tan, Juntao Heinecke, Shelby Xiong, Caiming Savarese, Silvio Wang, Huan Computation and Language Artificial Intelligence 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. |
| title | Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.14241 |