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In this video, I dive into Cache-Augmented Generation (CAG), a cutting-edge retrieval method that leverages server-side memory from OpenAI, Anthropic, and Google Gemini. I compare it head-to-head with traditional RAG (Retrieval-Augmented Generation) to see which approach delivers better results, faster speeds, and lower costs.
You’ll see detailed demos in n8n, including:
✅ Setting up workflows with OpenAI, Claude, and Gemini
✅ Uploading large documents to Gemini’s cache
✅ Comparing latency, cost, and accuracy between CAG and RAG
✅ Real-world tips for choosing the right architecture
If you're building AI tools that interact with knowledge bases, this one’s a must-watch!
00:00 Demo
03:30 RAG
06:57 CAG
15:55 Head to Head