Your LLM Doesn't Know When It's Wrong. A Second One Might.
The most dangerous failure mode in production LLMs isn't hallucination. It's confident hallucination. The model is dead wrong, and it's absolutely certain about it. Token entropy is low. Confidence scores look great. Your monitoring dashboard shows g...
theagentstack.hashnode.dev7 min read
The confident hallucination problem cuts deeper than most teams realize. Low token entropy is a feature, not a bug — the model learned to compress patterns into high-confidence outputs because that's what training optimizes for. The second-model verifier approach works, but it introduces its own failure mode: what happens when the verifier is confidently wrong? The real fix isn't just adding a second opinion — it's surfacing uncertainty as a first-class output. Models should be forced to say 'I don't know' with the same confidence they use to assert facts. Production systems that treat uncertainty as a signal, not noise, survive longer than those relying on consensus alone.