<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Langkit on Miguel Lameiro | Cybersecurity Blog &amp; Security Writeups</title><link>https://blog.lameiro0x.com/tags/langkit/</link><description>Recent content in Langkit on Miguel Lameiro | Cybersecurity Blog &amp; Security Writeups</description><generator>Hugo -- 0.161.1</generator><language>en-us</language><lastBuildDate>Thu, 07 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.lameiro0x.com/tags/langkit/index.xml" rel="self" type="application/rss+xml"/><item><title>Measuring Quality and Safety in LLM Applications</title><link>https://blog.lameiro0x.com/notes/ai-security/measuring-quality-and-safety-in-llm-applications/</link><pubDate>Thu, 07 May 2026 00:00:00 +0000</pubDate><guid>https://blog.lameiro0x.com/notes/ai-security/measuring-quality-and-safety-in-llm-applications/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Before adding controls to an LLM application, you need to know what is happening. Quality and safety measurement gives you that visibility.&lt;/p&gt;
&lt;p&gt;This course focuses on metrics and monitoring rather than runtime blocking. It uses chat datasets, WhyLogs, LangKit, custom UDFs, model-based scoring, and active monitoring patterns to inspect hallucinations, data leakage, toxicity, refusals, and prompt injection.&lt;/p&gt;
&lt;p&gt;The useful lesson is practical: do not treat &amp;ldquo;the model seems fine&amp;rdquo; as evidence. Log the interactions, compute signals, inspect critical examples, and evaluate filtered subsets.&lt;/p&gt;</description></item></channel></rss>