<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM Optimization on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/llm-optimization/</link><description>Recent content in LLM Optimization 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/llm-optimization/index.xml" rel="self" type="application/rss+xml"/><item><title>Performance Tuning and Caching Strategies</title><link>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/performance-caching/</link><pubDate>Tue, 30 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/performance-caching/</guid><description>&lt;h2 id="introduction-to-performance-tuning-and-caching"&gt;Introduction to Performance Tuning and Caching&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 9! So far, you&amp;rsquo;ve mastered the fundamentals of &lt;code&gt;any-llm&lt;/code&gt;, effortlessly switching between various LLM providers and handling different types of AI interactions. That&amp;rsquo;s fantastic! But as your applications grow and user demand increases, you&amp;rsquo;ll inevitably hit a critical crossroads: &lt;strong&gt;performance and cost&lt;/strong&gt;. Every interaction with an LLM provider incurs latency, consumes resources, and often, costs money. Imagine if every user asking the same question triggered a brand new, expensive API call – that would quickly become unsustainable!&lt;/p&gt;</description></item></channel></rss>