自动优化大模型提示词，让普通人也能轻松生成高效提示。
Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
- 用自然语言描述任务，系统自动生成优质提示词
- 在5类任务中表现优于或媲美现有工具，且更短更省资源
- 适合非专家快速上手，也支持未来功能扩展
大型语言模型（LLMs）在精心设计的提示词下表现最佳，但提示工程仍依赖人工、不一致，且对非专家不友好。我们提出 Promptomatix，一个自动提示词优化框架，能将自然语言任务描述转化为高质量提示词，无需手动调参或领域知识。该框架支持轻量级基于元提示的优化器和 DSPy 驱动的编译器，模块化设计便于未来扩展。系统分析用户意图，生成合成训练数据，选择提示策略，并通过成本感知目标优化提示词。在5个任务类别上的评估显示，Promptomatix 性能达到或超过现有库，同时减少提示长度与计算开销，使提示优化更可扩展、更高效。
原文摘要 · Abstract (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.
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