Promptomatix：面向大语言模型的自动提示优化框架

摘要

大型语言模型（LLMs）在精心设计的提示下表现最佳，然而提示工程仍然依赖手动操作，缺乏一致性，且对非专家用户不够友好。我们推出了Promptomatix，一个自动提示优化框架，能够将自然语言任务描述转化为高质量提示，无需手动调整或领域专业知识。Promptomatix支持基于轻量级元提示的优化器和DSPy驱动的编译器，其模块化设计便于未来扩展至更先进的框架。该系统通过分析用户意图、生成合成训练数据、选择提示策略，并利用成本感知目标优化提示。在五大任务类别上的评估显示，Promptomatix相较于现有库实现了竞争性或更优的性能，同时减少了提示长度和计算开销，使提示优化更具可扩展性和效率。

English

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：面向大语言模型的自动提示优化框架

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models

摘要

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