<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Extraction on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/extraction/</link><description>Recent content in Extraction on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 05 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/extraction/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 4: Basic Extraction and Understanding Results</title><link>https://ai-blog.noorshomelab.dev/langextract-guide-2026/04-basic-extraction-results/</link><pubDate>Mon, 05 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/langextract-guide-2026/04-basic-extraction-results/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 4! If you&amp;rsquo;ve made it this far, you&amp;rsquo;ve successfully set up your LangExtract environment and connected it to a Large Language Model (LLM) provider. That&amp;rsquo;s a huge step! Now, it&amp;rsquo;s time to put all that preparation to good use and perform your very first structured data extraction.&lt;/p&gt;
&lt;p&gt;This chapter is all about taking those initial, exciting &amp;ldquo;baby steps&amp;rdquo; into the world of LangExtract. We&amp;rsquo;ll focus on the core &lt;code&gt;extract&lt;/code&gt; function, learn how to define a simple schema to guide our LLM, and most importantly, understand how to interpret the results LangExtract provides. By the end of this chapter, you&amp;rsquo;ll be able to confidently extract specific pieces of information from text and inspect the quality of your extractions.&lt;/p&gt;</description></item></channel></rss>