<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Recommendation Systems on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/recommendation-systems/</link><description>Recent content in Recommendation Systems on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 30 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/recommendation-systems/index.xml" rel="self" type="application/rss+xml"/><item><title>Deep Dive into Embeddings</title><link>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/embeddings/</link><pubDate>Tue, 30 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/embeddings/</guid><description>&lt;h2 id="deep-dive-into-embeddings"&gt;Deep Dive into Embeddings&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI architect! In our journey with &lt;code&gt;any-llm&lt;/code&gt;, we&amp;rsquo;ve explored how to interact with various Large Language Models (LLMs) to generate text and understand their reasoning capabilities. Today, we&amp;rsquo;re taking a step back to dive into a fundamental concept that underpins many advanced AI applications: &lt;strong&gt;embeddings&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;This chapter will demystify embeddings, explaining what they are, why they&amp;rsquo;re incredibly useful, and how &lt;code&gt;any-llm&lt;/code&gt; provides a unified, straightforward way to generate them from different providers. We&amp;rsquo;ll move from theoretical understanding to practical application, showing you how to generate embeddings and use them for powerful tasks like semantic similarity. Get ready to transform text into numerical representations that unlock new dimensions of understanding!&lt;/p&gt;</description></item></channel></rss>