<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Chunking Strategies on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/chunking-strategies/</link><description>Recent content in Chunking Strategies 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/chunking-strategies/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 9: Tackling Long Documents with Chunking Strategies</title><link>https://ai-blog.noorshomelab.dev/langextract-guide-2026/09-chunking-strategies/</link><pubDate>Mon, 05 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/langextract-guide-2026/09-chunking-strategies/</guid><description>&lt;h2 id="chapter-9-tackling-long-documents-with-chunking-strategies"&gt;Chapter 9: Tackling Long Documents with Chunking Strategies&lt;/h2&gt;
&lt;p&gt;Welcome back, intrepid data explorer! So far, we&amp;rsquo;ve learned how to set up LangExtract, define schemas, and extract structured information from various texts. But what happens when your text isn&amp;rsquo;t a neat paragraph or a short email, but an entire legal contract, a research paper, or a lengthy financial report? These documents often exceed the &amp;ldquo;attention span&amp;rdquo; of even the most powerful Large Language Models (LLMs).&lt;/p&gt;</description></item></channel></rss>