In this video, I walk you through how to make your n8n RAG agents instantly smarter using re-rankers and metadata. If you've ever had your AI agent return the wrong information or pull irrelevant chunks from your vector database, this is the fix. Re-rankers help prioritize the most relevant chunks after your initial search, making the results dramatically more accurate. I also show you how to use metadata both when vectorizing and when retrieving, so your agents can filter and find exactly what they need with precision. These techniques are simple to implement but make a huge difference in how well your agents understand and respond. If you're building AI agents in n8n, this video will help you take them to the next level. Must be on version 1.98 or higher to access rerankers.

For a deeper discussion on building AI systems that provide meaningful ROI, let's chat here. In this video, I’ll show you how I’ve been saving hours each week by using n8n and OpenAI’s image generation API to automate marketing visuals. It’s surprisingly easy to set up — and in this walkthrough, I break it down step by step so you can follow along, even if you’re new to n8n or automation in general. If you're doing a lot of manual design work or just curious how AI can help streamline your workflow, this is a great place to start.