<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Constitutional-Ai on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/constitutional-ai/</link><description>Recent content in Constitutional-Ai on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 06 Apr 2026 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/constitutional-ai/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Engineering (11): Safety and Alignment</title><link>https://www.chenk.top/en/llm-engineering/11-safety/</link><pubDate>Mon, 06 Apr 2026 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/llm-engineering/11-safety/</guid><description>&lt;p>Safety has the worst signal-to-noise ratio of any topic in LLM engineering. There&amp;rsquo;s a lot of philosophy, a lot of marketing, and not a lot of engineering specifics. This chapter is the engineering specifics: what RLHF actually optimizes when it talks about &amp;ldquo;safety,&amp;rdquo; how refusal calibration breaks, what red-teaming looks like in practice, the hallucination measures that actually predict customer impact, and the small but significant 2024-2026 papers (Sleeper Agents, refusal as a feature direction, weak-to-strong generalization) that should change how you think about alignment in production.&lt;/p></description></item></channel></rss>