Your RAG isn't broken. It's just lying quietly. Retrieval works. The LLM sounds confident. Your users get an answer. But somewhere in that response, a claim contradicts the source document it was supposed to be grounded in. No error thrown. No flag raised. Just a confident, wrong answer, delivered at scale. This is the hallucination problem that doesn't get talked about enough. Not the obvious failures. The subtle ones. We've seen it across enterprise RAG deployments in legal tools, internal knowledge bases, customer-facing assistants. The retrieval pipeline performs. The LLM performs. And still, trust erodes the moment a user catches one bad answer. We're open sourcing LongTracer , our answer to this problem. LongTracer sits at the output layer of any RAG pipeline and verifies every claim in an LLM response against your source documents.…