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: 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: flowbyte.ai/
🔗 Connect with me on LinkedIn: www.linkedin.com/in/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