Agentic RAG: When Your Retrieval System Thinks for Itself
Originally published at adiyogiarts.com
Have you ever asked an AI a complex question, only to receive an answer that’s confidently, eloquently, and completely wrong? This phenomenon, the “polite hallucination,” is the critical flaw of many modern AI...
adiyogiarts.hashnode.dev9 min read
Ali Muwwakkil
One surprising insight is that the issue often isn't the retrieval system itself, but rather how it's integrated with decision-making agents. We've observed that when retrieval-augmented generation (RAG) architectures employ agentic behaviors to validate their own responses, there's a significant drop in these "polite hallucinations." By allowing retrieval systems to autonomously check and cross-verify data against multiple sources, you can dramatically improve accuracy. I wrote more about this here: enterprise.colaberry.ai/i/oc-hashnode-4dab489b