800+ Hours of Learning RAG + Agentic Design in 42 mins (n8n Masterclass)

Description

👉 Get our advanced RAG workflows and learn how to implement these patterns in n8n, in our community https://www.theaiautomators.com/?utm_source=youtube&utm_medium=video&utm_campaign=tutorial&utm_content=design_patterns There's no one-size-fits-all approach to RAG – and choosing the wrong pattern from the start sets you up to fail. In this comprehensive deep-dive, you'll learn the 9 essential RAG design patterns that cover every use case, from basic naive RAG to state-of-the-art multi-agent systems. I'll show you exactly when to use each pattern, why your model selection dictates your entire system design, and how to balance the critical trade-offs between intelligence, speed, and cost. 🎯 What You'll Master: ✅ Why model selection (small vs frontier LLMs) fundamentally shapes your RAG architecture ✅ How to match RAG patterns to your specific use case priorities ✅ When to prioritize speed vs accuracy vs cost in your system design ✅ 9 complete RAG design patterns with real n8n implementations ✅ Advanced multi-agent orchestration strategies ✅ Query routing, transformation, RAG fusion and verification techniques ✅ How to avoid the pitfalls that make RAG systems unreliable 💡 Real-World Use Cases Covered: 1. Customer-Facing Chatbot - Lightning-fast responses with query routing and answer verification 2. AI Assistant/Co-Pilot - Deep accuracy for legal departments with iterative retrieval 3. AI Automation - Background RAG systems embedded in content workflows 4. Fully Local RAG - Resource-constrained systems with optimal model selection 📋 The 9 RAG Design Patterns: 1. Naive RAG - The foundation (and why it's rarely enough) 2. Query Transformation, RAG Fusion & Verify Answer - Ensuring grounded, accurate responses 3. Iterative Retrieval - When one pass isn't enough 4. Adaptive Retrieval - Dynamic decision-making for retrieval 5. Agentic RAG - Non-deterministic systems with control over retrieval. 6. Hybrid RAG - Systems that can retrieve from different types of knowledgebases (graph, structured, semantic etc) 7. Multi-Agent RAG with Sub-Agents - Distributing cognitive load 8. Multi-Agent RAG with Sequential Chaining - Specialized agents in deterministic flows 9. Multi-Agent RAG with Routing - Intelligent query distribution Plus bonus coverage of: Self-RAG, CorrectiveRAG, Guardrails, Human-in-the-Loop, Context Expansion, and Deep RAG patterns! 🔗 Useful Links: Context Expansion Video: https://www.youtube.com/watch?v=y72TrpffdSk RAG at Scale Video: https://www.youtube.com/watch?v=sn0SjjkRhxI&t=285s Dynamic Hybrid Search: https://www.youtube.com/watch?v=FgUJ2kzhmKQ&t=613s ⏱️ Timestamps: 00:00 - Intro 02:48 - Use Cases & Priorities 07:21 - 1. Naive RAG 10:13 - 2. Query Transformation & Verify Answer 19:48 - 3. Iterative Retrieval 21:14 - 4. Adaptive Retrieval 25:25 - 5-6. Agentic + Hybrid RAG 31:24 - 7. Multi-Agent RAG with Sub-Agent 36:10 - 8. Multi-Agent RAG with Sequential Chaining 38:28 - 9. Multi-Agent RAG with Routing 40:02 - Bonus: Other Patterns 💬 Questions or Comments? Drop them below! I read every comment and love hearing about what RAG systems you're building and which patterns work best for your use cases. 📢 If you found this valuable: 👍 Like this video 🔔 Subscribe for more advanced AI automation content 📤 Share with someone building RAG systems