The AI Hallucination Is Usually a Trust-System Failure
A confident answer enters a workflow with no source, no uncertainty marker, and no clear owner for checking it. The error is not discovered until someone has to repair trust downstream.
The recognizable symptom
The team debates whether the model is smart enough while the actual process still treats fluent text as evidence. A polished answer receives more trust than a cautious one, even when neither has a source.
The mechanism underneath
The system has collapsed generation and judgment into one step. It asks the model to produce an answer and silently assigns the human the job of discovering whether the answer deserved to exist.
What we built to contain it
We separate retrieval, judgment, and outcome logging. Important outputs carry provenance, a stated confidence boundary, and an explicit falsifier. When the claim misses, the miss is published with the same weight as the hit.
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