提示工程与结构化提示

出处: Prompt Engineering vs Structured Prompts

发布: 2026年3月11日

📄 中文摘要

在使用小型标记语言(SML)描述场景的实验中,作者探索了相较于自由文本提示的优势。通过结构化的方式,SML能够更清晰地定义角色、环境和事件,从而提高生成内容的准确性和一致性。示例代码展示了如何使用SML来构建场景,这种方法可能为AI生成内容提供新的思路和工具,尤其是在需要复杂场景描述的应用中。

📄 English Summary

Prompt Engineering vs Structured Prompts

The author experiments with using a small markup language (SML) to describe scenes instead of writing free-text prompts. This structured approach allows for clearer definitions of characters, environments, and events, potentially enhancing the accuracy and consistency of generated content. Example code illustrates how to construct scenes using SML, suggesting that this method could offer new ideas and tools for AI content generation, particularly in applications requiring complex scene descriptions.

Powered by Cloudflare Workers + Payload CMS + Claude 3.5

数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等