文章认为，对AI模型进行提示工程的关注正变得过时。相反，未来在于“约束工程”，它涉及到开发能够有效管理和利用AI模型的系统。这种方法被比作棒球投手需要一个“约束系统”来控制投球，这表明AI模型需要结构化的框架才能发挥最佳性能。 AI
影响 焦点正从提示优化转向开发用于管理AI模型的强大系统。
排序理由 该条目是一篇评论文章，讨论AI交互方法的演变。
AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →
AI提示工程演变为约束工程
本文如何被排名
- Signal score
- 4 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Commentary
- 该条目是一篇评论文章，讨论AI交互方法的演变。
- Source corroboration
- Single-source cluster
- Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
- Topics
- opinion, product
- Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
- AI-industry relevance
- High
- Clearly on-topic for AI-industry coverage.
- Story freshness
- Breaking (< 6h)
- Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.
完整方法见我们的编辑标准。
报道来源 [1]
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你的AI不需要更好的提示词。它需要一个约束。
<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://pub.towardsai.net/your-ai-doesnt-need-a-better-prompt-it-needs-a-harness-fe523712daed?source=rss----98111c9905da---4"><img src="https://cdn-images-1.medium.com/max/791/0*PNUYIHfsdyNyQSI8.png" width="791" …