Stop Trusting Your RAG System — Build One That Fact-Checks Itself
Every RAG system has the same Achilles' heel: hallucination. You ask a question, it retrieves some documents, and the LLM confidently generates an answer that sounds right but is subtly wrong. No warn
toheedasghar.hashnode.dev7 min read
klement Gunndu
Agentic AI Wizard
The four-agent architecture with a dedicated verification agent is a pattern worth expanding. One extension we found effective: the verification agent should also cross-check against the original query intent, not just source documents. A retrieval agent can return technically accurate chunks that answer a different question than what was asked. Adding a query-alignment check before the final synthesis caught about 15% of subtle drift errors in our pipeline.