<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>OpenAI API on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/openai-api/</link><description>Recent content in OpenAI API on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 24 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/openai-api/index.xml" rel="self" type="application/rss+xml"/><item><title>Crafting Precise Prompts: System Messages, Delimiters, and Output Control</title><link>https://ai-blog.noorshomelab.dev/prompt-agent-ai-2026-guide/crafting-precise-prompts/</link><pubDate>Mon, 06 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/prompt-agent-ai-2026-guide/crafting-precise-prompts/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome back, fellow AI adventurer! In Chapter 1, we took our first steps into the exciting world of prompt engineering, learning how to ask Large Language Models (LLMs) basic questions and get meaningful responses. You saw the raw power of these models, but perhaps also noticed that they can sometimes be a bit&amp;hellip; creative, or even inconsistent.&lt;/p&gt;
&lt;p&gt;In production environments, &amp;ldquo;creative&amp;rdquo; and &amp;ldquo;inconsistent&amp;rdquo; are often code words for &amp;ldquo;unreliable&amp;rdquo; and &amp;ldquo;buggy&amp;rdquo;! To build robust AI applications, we need to move beyond simple questions and learn how to guide LLMs with precision and control. This chapter is all about transforming your prompts from casual conversations into structured, instruction-driven directives. We&amp;rsquo;ll dive into three fundamental techniques: &lt;strong&gt;System Messages&lt;/strong&gt; for defining the LLM&amp;rsquo;s role and rules, &lt;strong&gt;Delimiters&lt;/strong&gt; for clearly separating different parts of your input, and &lt;strong&gt;Output Control&lt;/strong&gt; for ensuring the LLM delivers responses in a predictable, parseable format.&lt;/p&gt;</description></item><item><title>Integrating Your First AI Agent: Claude Code or Codex</title><link>https://ai-blog.noorshomelab.dev/kanbots-ai-worktrees-2026/integrate-first-ai-agent/</link><pubDate>Sun, 24 May 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/kanbots-ai-worktrees-2026/integrate-first-ai-agent/</guid><description>&lt;p&gt;This chapter marks a pivotal moment for Kanbots. We&amp;rsquo;re moving beyond a static Kanban board and injecting intelligence by integrating our first AI agent. You&amp;rsquo;ll learn how to connect an AI model like Claude Code or a modern OpenAI equivalent (e.g., GPT-4o) to a Kanban card. This enables the agent to perform specific tasks, such as generating code, within its dedicated git worktree. By the end of this milestone, your Kanbots application will be able to dispatch a task to an AI agent, have that agent generate content (like a simple code file), and observe the results directly within the isolated worktree associated with your Kanban card. This lays the foundation for powerful, automated development workflows.&lt;/p&gt;</description></item></channel></rss>