<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Evals &amp; Observability — The Latent Daily</title><description>Evaluating, testing and monitoring AI systems.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/evals-observability.xml" rel="self" type="application/rss+xml"/><item><title>How to build an evaluation set for an LLM application</title><link>https://thelatentdaily.com/guides/how-to-build-an-evaluation-set-for-llm-apps/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/how-to-build-an-evaluation-set-for-llm-apps/</guid><description>The most important tool in AI engineering: how to collect test cases, define success criteria, score outputs automatically and by hand, and run evals on every change.</description><pubDate>Sun, 18 Oct 2026 04:43:00 GMT</pubDate><category>Evals &amp; Observability</category><category>evals</category><category>testing</category><category>developers</category><category>quality</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>LLM-as-a-judge explained: using AI models to grade AI output</title><link>https://thelatentdaily.com/articles/llm-as-a-judge-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/llm-as-a-judge-explained/</guid><description>Language models can score other models&apos; answers at scale. How LLM-as-a-judge works, its known biases, and how to design rubrics and checks that make it reliable.</description><pubDate>Sun, 18 Oct 2026 04:42:00 GMT</pubDate><category>Evals &amp; Observability</category><category>evals</category><category>llm-as-a-judge</category><category>testing</category><category>research</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>