In this video, I walk you through a no-code RAG (Retrieval-Augmented Generation) workflow I built using n8n. I’ll show you how I take YouTube video transcripts, store them in a Supabase vector database, and enrich them with metadata like video titles, URLs, and timestamps. This allows the RAG agent to tell me exactly where the retrieved answer came from — including which video, the link to that video, and even the moment in the video it pulled the data from. I also break down what metadata is, why it matters, and how it dramatically improves retrieval accuracy and context. If you’re building any kind of AI assistant or agent, this will be a foundational concept to understand — and I make it as simple and practical as possible. Google Sheet Template

Here’s how it works: Over two weeks, we'll work together to build your first AI agents. No fluff. No theory. Just hands-on building with me and the AI Automation Society team. By the end, you'll have working AI agents in your portfolio and the confidence that comes from actually doing it. You also have the foundation you need to begin charging businesses for developing custom AI agents. And the best part? The whole class is FREE (with a small catch). If you're ready to stop sitting on the sidelines, click below to grab your spot. ANNOUNCING: An automation accelerator for beginners (it's free) Talk soon, Nate PS: Some of my students are making $10,000 + from creating automations like these. If you want to start your own AI automation agency one day, this is the perfect first step.