Promptomatix: An Automatic Prompt Optimization Framework for Large
  Language Models
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.
6882978a6a54dd1e77daa9c2
Rithesh Murthy
claimed_verified
2025-07-25T08:38:46.525Z
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/avatars/2c2c4b75dd0199478dc0fbc72f9bcadc.svg
rmurthy
user
rmurthy
6882978a6a54dd1e77daa9c3
Ming Zhu
6882978a6a54dd1e77daa9c4
Liangwei Yang
6882978a6a54dd1e77daa9c5
Jielin Qiu
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Juntao Tan
6882978a6a54dd1e77daa9c7
Shelby Heinecke
6882978a6a54dd1e77daa9c8
Caiming Xiong
6882978a6a54dd1e77daa9c9
Silvio Savarese
6882978a6a54dd1e77daa9ca
Huan Wang
Promptomatix: An Automatic Prompt Optimization Framework for Large
  Language Models
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.
Promptomatix automates prompt optimization for Large Language Models, improving performance and efficiency across various tasks.
Promptomatix meta-prompt-based optimizer DSPy-powered compiler synthetic training data prompting strategies cost-aware objectives