<?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>Posts on Jack Monte — AI Engineer</title><link>https://jackmonte.com/posts/</link><description>Recent content in Posts on Jack Monte — AI Engineer</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 21 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jackmonte.com/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>I Built an Eval Harness for My RAG Pipeline. Here's What the Numbers Revealed.</title><link>https://jackmonte.com/posts/eval-harness-rag-pipeline/</link><pubDate>Thu, 21 May 2026 00:00:00 +0000</pubDate><guid>https://jackmonte.com/posts/eval-harness-rag-pipeline/</guid><description>&lt;p&gt;Most RAG pipelines ship without any systematic way to measure whether they&amp;rsquo;re actually working. You run a few manual queries, the answers look reasonable, and you move on. The problem is that &amp;ldquo;looks reasonable&amp;rdquo; doesn&amp;rsquo;t tell you where the system is failing, how often it fails, or whether it would fail on the queries your users actually send.&lt;/p&gt;
&lt;p&gt;That was the state of my rag-starter pipeline at the end of Week 4. It could answer questions grounded in a document corpus, and it correctly said &amp;ldquo;I don&amp;rsquo;t know&amp;rdquo; when context was insufficient. But I had no numbers. I didn&amp;rsquo;t know if the retrieval was finding the right chunks, whether Claude was staying faithful to the context, or whether the answers were actually addressing the questions asked.&lt;/p&gt;</description></item></channel></rss>