Why I Built an AI Assistant for My Blog

I wanted my blog to do more than list posts. I wanted visitors to be able to ask natural questions about me, my work, and anything I’ve written, and get answers that cite the right articles without me hand. In my previous post I laid the infrastructure groundwork by running n8n on Azure as an orchestration layer. This article goes deeper into how I assembled the chat assistant itself and wired it to semantic search so it actually “knows” my content rather than doing a brittle keyword lookup.

AI is no longer an abstract promise of the future. It’s here, embedded into enterprise workflows, products, and decision-making processes. From Microsoft Copilot to ChatGPT and domain-specific assistants, businesses are adopting AI at an unprecedented pace. But too often, “AI” is used as a catch-all term for a wide range of technologies. To lead the next transformation wave, organizations must move beyond generic AI adoption and toward agentic AI, a more autonomous, goal-driven form of AI that’s ready to take on real work.

In today’s AI era, you’ve likely interacted with Microsoft Copilot, ChatGPT, or Claude.ai, tools powered by advanced Large Language Models (LLMs). These models excel at understanding and generating human-like text based on vast amounts of training data. However, while LLMs are impressive at reasoning and answering general questions, they fall short when it comes to performing real-world tasks or retrieving live, domain-specific information.

This is where the Model Context Protocol (MCP) comes in.