# RAG-based Verification Cross-reference outputs against trusted sources. Standard [[RAG]] retrieves context to help generation. RAG-based verification flips this: retrieve context to check generation. Did the model's claims actually appear in the source material? Tools like Amazon RefChecker break outputs into knowledge triplets (subject, predicate, object) and verify each against the retrieved documents. Google's DataGemma integrates real-world data from Data Commons for fact-checking. This reduces hallucinations but doesn't eliminate them. The model can still misinterpret sources, make incorrect inferences, or generate claims that sound like they're from the source but aren't. The hard part: you need reliable external sources and solid retrieval infrastructure. Garbage in, garbage out. If your knowledge base has errors, verification inherits them. Works best in domains with authoritative sources: legal databases, financial filings, medical literature. Weaker in areas where "truth" is contested or sources conflict. --- Links: - [[AI Verification]] - [[RAG]] - [[AI Inference Infrastructure]] - [[Vector Databases]] --- #deeptech #kp