https://blog.chuanxilu.net/en/tags/prompt-engineering/Recent content in Prompt Engineering on Chuanxilu for Skilled Homo sapiensHugoen-USWed, 20 May 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/05/tdd-pipeline-v07-refinement-experiment/Wed, 20 May 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/05/tdd-pipeline-v07-refinement-experiment/I built a ruler. The ruler measured 'redundancy is harmful.' Then I used that ruler to trim the ruler's own redundancy. I deleted the operational steps from my AI skill files, keeping only principles and counterexamples. The model reconstructed the deleted steps on its own — output quality didn't drop.https://blog.chuanxilu.net/en/posts/2026/05/why-articulation-upgrade-and-takeaways/Sun, 17 May 2026 09:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/05/why-articulation-upgrade-and-takeaways/Upgrading the Why Articulation template based on A/B test data: replacing explicit questions with open-ended reasoning plus self-check, keeping mandatory tone and negative-only examples. Three transferable prompt engineering lessons.https://blog.chuanxilu.net/en/posts/2026/05/ab-test-positive-examples-harm/Fri, 15 May 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/05/ab-test-positive-examples-harm/Why do positive examples make AI output worse? A 4-variable A/B test on Why Articulation structure, tone, position, and example type found that demonstrations hurt — echoing Anthropic's alignment research.https://blog.chuanxilu.net/en/posts/2026/05/anthropic-alignment-to-prompt-design/Thu, 14 May 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/05/anthropic-alignment-to-prompt-design/Anthropic discovered that teaching models "why" works better than teaching them "what" — misalignment dropped from 22% to 3%. This insight from safety training applies to everyday prompt design too.