#46 Max: Make Your n8n RAG Agents 10x Smarter with Reranking & Metadata
Tired of your n8n RAG agent confidently giving you wrong or irrelevant answers? 😫 The problem is that basic vector search isn't smart enough. We're revealing two pro techniques that will make your AI agents 10x smarter.We’ll talk about:A deep dive into two powerful techniques—Reranking and Metadata Filtering—to fix failing RAG agents in n8n.How Vector Reranking acts as a quality control manager, ensuring your AI gets the most contextually relevant information, not just mathematically similar results.A step-by-step guide to implementing Cohere's powerful reranker model directly within n8n's Supabase Vector Store node.The power of Metadata Filtering for "surgical precision," and how to use AI to automatically structure and tag your documents for better retrieval.The complete data preparation workflow: from processing a PDF to creating a structured, metadata-rich knowledge base in Supabase.Keywords: n8n, RAG, Retrieval-Augmented Generation, AI Agents, Reranking, Cohere, Metadata, Supabase, Vector Database, OpenAI, PDF Parsing, AI Data Processing, n8n tutorialLinks:Newsletter: Sign up for our FREE daily newsletter.Our Community: Get 3-level AI tutorials across industries.Join AI Fire Academy: 500+ advanced AI workflows ($14,500+ Value)Our Socials:Facebook Group: Join 231K+ AI buildersX (Twitter): Follow us for daily AI dropsYouTube: Watch AI walkthroughs & tutorials