n8n Hybrid RAG: The #1 Fix for Knowledge Base Inaccuracy

Building a RAG (Retrieval-Augmented Generation) agent that gives inaccurate answers is the most common mistake for developers. If your n8n RAG agent is failing, it's because basic vector search is not enough. This tutorial provides the fix. Learn how to build a truly advanced RAG agent with n8n and Supabase by implementing a powerful Hybrid Search method. This step-by-step guide will show you how to combine keyword search with vector search to create a system that provides accurate results. This is the key to building a production-ready AI agent that you can actually trust. This RAG tutorial covers the entire process, from setting up Supabase for vector storage to deploying a fully functional Hybrid Search engine within your n8n workflow. Stop getting bad results and learn the technique that solves the accuracy problem for good. ━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔗 RESOURCES & LINKS 💬 Download the full resource in Skool: https://www.skool.com/erictech 📅 Work With Me New Projects - Free Strategy Call: https://calendar.app.google/sB9KrJP6e... Technical Consultation (Paid 1:1): https://calendar.app.google/BU9D589X3... 🤝 Let's Connect LinkedIn:   / ericwtech   ━━━━━━━━━━━━━━━━━━━━━━━━━━ Timestamps: 00:00 - Why Vector RAG is Inaccurate? 02:27 - How Hybrid Search works? 03:34 - Setup Supabase Hybrid Search 10:24 - Setup Hybrid RAG in n8n 15:08 - Demo: An Accurate n8n RAG Agent