{"ID":2893648,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14241","arxiv_id":"2507.14241","title":"Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.14241","url_pdf":"https://arxiv.org/pdf/2507.14241v3","authors":"[\"Rithesh Murthy\",\"Ming Zhu\",\"Liangwei Yang\",\"Jielin Qiu\",\"Juntao Tan\",\"Shelby Heinecke\",\"Caiming Xiong\",\"Silvio Savarese\",\"Huan Wang\"]","published":"2025-07-17T18:18:20Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}