This RAG Trick Makes Your AI Agents WAY More Accurate (n8n)

👉 Get our n8n Context Expansion engine and learn how to customize it, in our community https://www.theaiautomators.com/?utm_... Your RAG agent is probably lying to you. Not because it's hallucinating — but because it's making decisions with incomplete information. Vector search returns isolated chunks, but strips away the document structure that gives those chunks meaning. Your agent sees "tennis elbow is covered by the insurance policy" but has no idea it's reading from the Policy Exclusions section. That's the fundamental problem with RAG systems: lost context. In this video, I'll show you how to solve it with Context Expansion — the ability for your AI agent to intelligently retrieve sections, subsections, or entire documents based on document structure, not just isolated chunks. We built 5 different context expansion approaches in n8n: ✅ Full Document Expansion — Load the entire file when chunks point to the right doc ✅ Neighbor Expansion — Fetch chunks before and after the candidate chunk ✅ Section Expansion — Pull complete sections based on markdown headings ✅ Parent Expansion — Retrieve everything under a parent heading ✅ Agentic Expansion — Use document hierarchy to navigate and fetch multiple sections The secret? A custom chunking system that extracts document structure, maps it to chunk indexes, and injects contextual metadata — so your agent always knows where it is in the document. 🔗 Related Links & Resources: Contextual Embeddings Deep Dive:    • Two NEW n8n RAG Strategies (Anthropic’s Co...   RAG at Scale Tutorial:    • We Unlocked n8n's BEAST Mode (5,000 Files/...   Markdown Splitting Guide: https://python.langchain.com/docs/how... What You'll Learn: ✅ Why vector search alone causes RAG hallucinations ✅ The 5 approaches to context expansion (and when to use each) ✅ How to extract and map document structure to chunk indexes ✅ Building smart markdown chunking in n8n (headings first, then recursive split) ✅ Using Supabase edge functions for dynamic context retrieval ✅ Injecting contextual snippets without per-chunk LLM calls ✅ Creating document hierarchies that agents can navigate ✅ Tracking page numbers for traceability Timestamps: 00:00 Demo 04:16 The Problem 07:06 Option 1: Full Document Expansion 13:19 Option 2: Neighbor Expansion 16:09 Option 3&4: Section & Parent Expansion 28:53 Option 5: Agentic Expansion This isn't just theory — this is production-grade context expansion that reduces hallucinations, delivers comprehensive answers, and scales without burning through LLM costs. If you want accurate RAG systems, context expansion isn't optional. It's essential.