Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models

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 efficient.