Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
This addresses the challenge of making prompt engineering accessible and efficient for users, especially non-experts, though it appears incremental as it builds on existing optimization techniques.
The authors tackled the problem of manual and inconsistent prompt engineering for large language models by introducing Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts, achieving competitive or superior performance across 5 task categories while reducing prompt length and computational overhead.
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.