Once You Know This, Building RAG Agents Becomes Easy in n8n

Full courses + unlimited support: https://www.skool.com/ai-automation-society-plus/about All my FREE resources: https://www.skool.com/ai-automation-society/about Apply for my YT podcast: https://podcast.nateherk.com/apply Work with me: https://uppitai.com/ My Tools💻 14 day FREE n8n trial: https://n8n.partnerlinks.io/22crlu8afq5r Code NATEHERK to Self-Host n8n for 10% off (annual plan): http://hostinger.com/nateherk Voice to text: https://ref.wisprflow.ai/nateherk In this video, I break down the different ways you can handle retrieval and context in RAG systems when building AI agents in n8n. I start by explaining why chunk-based retrieval often causes hallucinations and inaccurate answers, especially when the agent is missing full context. Then I walk through three practical approaches I actually use in real systems: using filters to narrow context, using SQL queries to pass full and structured context to the agent, and using vector search when semantic matching makes sense. For each approach, I explain what it is, how it works, when it breaks down, and when it is the right tool for the job. Sponsorship Inquiries: 📧 sponsorships@nateherk.com TIMESTAMPS 00:00 What We’re Covering 01:00 The Problem with Chunk Based Retrieval 03:47 1) Filters 07:47 2) SQL Query 11:20 3) Full Context 15:40 4) Vector Search 17:00 Want to Master AI Automations?