Why Simple RAG Fails: Controlled AI Agents with N8N & Voiceflow скачать в хорошем качестве

Why Simple RAG Fails: Controlled AI Agents with N8N & Voiceflow

In this step-by-step build, we move from a basic Retrieval-Augmented Generation setup to a Controlled RAG architecture that routes every customer question to the right knowledge slice and delivers spot-on answers—no hallucinations, no confusion. What you’ll see in the video • Why simple RAG fails when you have thousands of similar product docs • Classification agent that tags each query in real time • Rule-based switching to isolate product groups • Tool-agents for Finance, Audit, Accounting & General queries (real client workflow in n8n) • How deep-dive retrieval grabs the exact information from a PDF 🔗 n8n templates: https://bit.ly/controlled_rag ⏱️ Chapters 00:00 Why Simple RAG fails frequently 01:15 Demo Start 02:15 Why do we need controlled RAG? 04:28 Voiceflow customer experience route 05:53 n8n scenario flow 07:12 AI classificator 09:28 General information retrieval and AI agents 15:04 Custom memory variable 17:30 Finding information about exact product (2nd scenario) 22:40 Testing & Demo 28:58 Final Thoughts Need help? 🔗 Reach out for advice: https://flowbyte.ai 🔗 Connect with me on LinkedIn:   / maksims-sics   🔗 Subscribe for more content:    / @maksimssics   Let’s grow together! 👍 Like this video if it helped 🔔 Subscribe for weekly AI automation tutorials 💬 Drop your questions or share what you’re building in the comments #chatbots #ecommerceai #vectordatabase #n8n #ai