Make Your AI Agents 10x Smarter with Hybrid Retrieval (n8n)

Description:

👉 Get access to our Hybrid Retrieval n8n workflows in our community Vector Search isn't the silver bullet everyone thinks it is. After working with hundreds of community members who are building production-grade RAG agents, we keep seeing the same failures. Questions that should be simple get hallucinated answers. Queries that need exact matches return "similar" results. Systems that work in demos break in production. Vector search is brilliant for semantic queries—but there's an entire category of questions that need completely different retrieval strategies. In this deep-dive, I'll show you 9 real-world examples where Vector Search fails, and demonstrate the retrieval engineering strategies that actually work in production. 🎯 What You'll Learn: ✅ Why similarity ≠ relevance (and what to do about it) ✅ 9 types of queries where vector search breaks down ✅ Exact match strategies for error codes and IDs ✅ Structured data approaches for tabular queries ✅ Aggregation techniques for counting and computing ✅ GraphRAG for global knowledge patterns ✅ Multi-hop reasoning with knowledge graphs ✅ Multimodal RAG for image retrieval ✅ Handling false premise questions without hallucinations ✅ Complete evaluation strategies using DeepEval 🔗 Useful Links: Amit Verma Article: IBM Know Your RAG Research - CRAG Benchmark - ⏱️ Timestamps: 00:00 - The Vector Search Myth 05:20 - #1 Summary Questions 12:27 - #2 Simple Questions 15:25 - #3 Simple Questions with Conditions 19:55 - #4 Aggregation Questions 20:41 - #5 Global Questions 22:21 - #6 Multi-Hop Questions 23:59 - #7 Multi-Modal Questions 25:12 - #8 Post-Processing Questions 26:18 - #9 False Premise Questions 💬 Questions or Comments? What retrieval challenges are you facing in your RAG systems? Which of these 9 problems have you encountered? Drop your thoughts below!