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

TL;DR
Promptomatix is an automated framework that optimizes prompts for large language models, making prompt engineering accessible, efficient, and scalable without manual tuning or domain expertise.
Contribution
It introduces a modular system combining meta-prompt optimization and compilation to automate prompt creation for diverse tasks.
Findings
Achieves competitive or superior performance on 5 task categories
Reduces prompt length and computational overhead
Supports future extension to advanced frameworks
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…
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Taxonomy
TopicsMachine Learning and Data Classification · Machine Learning in Materials Science · Topic Modeling
