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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rmurthy
user
rmurthy
6882978a6a54dd1e77daa9c3
Ming Zhu
6882978a6a54dd1e77daa9c4
Liangwei Yang
6882978a6a54dd1e77daa9c5
Jielin Qiu
6882978a6a54dd1e77daa9c6
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.