<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Automatic Prompt Optimization on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/automatic-prompt-optimization/</link><description>Recent content in Automatic Prompt Optimization on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 06 Nov 2025 22:00:00 +0530</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/automatic-prompt-optimization/index.xml" rel="self" type="application/rss+xml"/><item><title>Project 1: Optimizing a Basic QA Agent with Prompt Tuning</title><link>https://ai-blog.noorshomelab.dev/agentic-lightening-guide/project-optimizing-basic-qa-agent/</link><pubDate>Thu, 06 Nov 2025 22:00:00 +0530</pubDate><guid>https://ai-blog.noorshomelab.dev/agentic-lightening-guide/project-optimizing-basic-qa-agent/</guid><description>&lt;h2 id="project-1-optimizing-a-basic-qa-agent-with-prompt-tuning"&gt;Project 1: Optimizing a Basic QA Agent with Prompt Tuning&lt;/h2&gt;
&lt;p&gt;This project will guide you through building a simple Question-Answering (QA) agent and then using Agentic Lightening to optimize its performance through &lt;strong&gt;Automatic Prompt Optimization (APO)&lt;/strong&gt;. This is a classic example of how Agentic Lightening can iteratively refine an agent&amp;rsquo;s behavior by adjusting its interaction with an LLM, without needing to fine-tune the LLM itself.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Clear Objective:&lt;/strong&gt; To create a QA agent that can accurately answer factual questions and optimize its performance by dynamically tuning its system prompt.&lt;/p&gt;</description></item></channel></rss>