Next-token prediction samples from a probability distribution, so some uncertainty is inherent. But many hallucinations are reducible system failures: missing retrieval, no verification, no abstention policy. This simulator shows how mitigations lower risk without ever reaching zero. All numbers are deterministic illustrations, not measurements.
Token-Branch Confidence
Generated Answer simulated
Ungrounded vs Grounded Workflow
The left panel ignores retrieval and verification; the right applies your interventions. Risk updates instantly as controls change.
Ungrounded Model
Grounded Workflow
Claim–Evidence Matrix
Select a claim row to inspect simulated supporting snippets, contradictions, and the recommended action.
Knowledge Check
Why this matters
A language model does not look facts up by default; it continues text with plausible tokens. When context is thin and temperature is high, fluent-but-unsupported claims become likely. Retrieval grounds claims in documents, tools handle arithmetic and lookups, cross-checking catches contradictions, and abstention policies convert low confidence into honest uncertainty. These mitigations reduce risk substantially — they do not eliminate it, and calibrated humility remains part of good system design. High confidence is not proof: calibration means confidence should track accuracy, and it often does not without deliberate engineering.