提示工程正演变为一门系统性学科，超越简单的指令，发展出用于优化大型语言模型（LLM）输出的高级技术。DSPy 等工具可自动进行提示结构和示例选择，将提示编写转变为一个程序化过程。建议开发者像对待代码一样对待提示，专注于结构化格式，如 XML 标签、精选的少样本示例以及明确的推理步骤（如思维链），以在 LLM 性能方面实现可靠、可衡量的改进。 AI
影响 自动化的提示优化和结构化技术将提高 LLM 在生产应用中的可靠性和性能。
排序理由 文章讨论了提示工程的高级技术和工具，将其视为一门系统性学科而非一门艺术，并提到了面向未来的能力。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
<p>Prompt engineering has emerged as a critical skill for developers and professionals working with large language models. I explored various prompting techniques including zero-shot, few-shot, and chain-of-thought prompting to improve AI response quality and accuracy. Learning h…
<blockquote> <p><em>This article was originally published on <a href="https://dingjiu1989-hue.github.io/en/ai/prompt-optimization-techniques.html" rel="noopener noreferrer">AI Study Room</a>. For the full version with working code examples and related articles, visit the original…
<blockquote> <p><em>This article was originally published on <a href="https://dingjiu1989-hue.github.io/en/ai/prompt-engineering-advanced.html" rel="noopener noreferrer">AI Study Room</a>. For the full version with working code examples and related articles, visit the original po…