Promptomatix: An Automatic Prompt Optimization Framework for Large
  Language Models
Authors: name: Rithesh Murthy, name: Ming Zhu, name: Liangwei Yang, name: Jielin Qiu, name: Juntao Tan, name: Shelby Heinecke, name: Caiming Xiong, name: Silvio Savarese, name: Huan Wang

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.