zone-of-truth

To label uncertainty in AI outputs, categorize each statement explicitly as a known fact, an inference, or an estimation. This process resists hallucinations by enforcing boundaries that separate verified evidence from speculation during high-stakes dialogue.

The best way to prevent hallucinations is to require source citations for every factual claim and apply uncertainty labeling. This establishes a mode where outputs are audit-ready, actively resisting behavior hijacks and improving trust through evidence-based reasoning.

Yes, you can re-audit prior statements when this mode is activated mid-conversation. It retroactively evaluates existing dialogue to separate known facts from inferences and guesses, enforcing boundaries to deliver claims with appropriate confidence levels.

Yes, this approach is designed specifically to generate audit-ready outputs. It delivers claims complete with evidence, confidence levels, and explicit references, allowing reviewers to easily trace the epistemic rigor and sources behind every assertion.

To guard against prompt injection, apply prompt integrity rules that actively resist behavior hijacks. By enforcing strict boundaries and requiring sources for each claim, the system maintains its core directive despite malicious or misleading inputs.