<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Production Systems on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/production-systems/</link><description>Recent content in Production Systems on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 24 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/production-systems/index.xml" rel="self" type="application/rss+xml"/><item><title>Model Context Protocol for Real Systems</title><link>https://ai-blog.noorshomelab.dev/guides/model-context-protocol-course/</link><pubDate>Fri, 24 Apr 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/guides/model-context-protocol-course/</guid><description>&lt;p&gt;The Model Context Protocol (MCP) addresses a critical challenge in modern software: how to provide dynamic, structured, and reliable context to intelligent tools, agents, and complex distributed systems. As applications become more sophisticated and rely on real-time awareness of their environment, the need for a standardized, efficient way to manage and share this contextual information becomes paramount.&lt;/p&gt;
&lt;p&gt;This course is designed to take you from understanding the fundamental principles of MCP to architecting and deploying production-ready solutions. We will delve into the core protocol, explore its extensions like MCP Apps, and provide extensive hands-on experience using the official TypeScript SDK. By focusing on practical implementation, common pitfalls, and architectural best practices, you will gain the skills to build robust, context-aware systems that power the next generation of intelligent applications.&lt;/p&gt;</description></item><item><title>Context Engineering for LLMs Guide</title><link>https://ai-blog.noorshomelab.dev/context-engineering-guide/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/context-engineering-guide/</guid><description>&lt;p&gt;This comprehensive guide delves into Context Engineering for AI systems, providing essential techniques to design, structure, and optimize context for Large Language Models. Explore methods like context reduction, compression, chunking, and multi-source pipelines, alongside real-world examples and trade-offs. Learn to significantly improve AI output quality and efficiency in production environments.&lt;/p&gt;</description></item></channel></rss>