<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Search Engine on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/search-engine/</link><description>Recent content in Search Engine on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 17 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/search-engine/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 22: Project: Developing a Semantic Search Engine with Embeddings</title><link>https://ai-blog.noorshomelab.dev/ai-ml-career-path-2026/project-semantic-search/</link><pubDate>Sat, 17 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-ml-career-path-2026/project-semantic-search/</guid><description>&lt;h2 id="chapter-22-project-developing-a-semantic-search-engine-with-embeddings"&gt;Chapter 22: Project: Developing a Semantic Search Engine with Embeddings&lt;/h2&gt;
&lt;p&gt;Welcome to an exciting hands-on project that brings together several concepts we&amp;rsquo;ve explored: embeddings, natural language processing, and practical application! In this chapter, you&amp;rsquo;ll learn how to build a semantic search engine from the ground up. Unlike traditional keyword-based search that relies on exact word matches, semantic search understands the &lt;em&gt;meaning&lt;/em&gt; and &lt;em&gt;context&lt;/em&gt; of your query, providing far more relevant results.&lt;/p&gt;</description></item></channel></rss>