aif-grounded

To prevent AI hallucinations in critical contexts, enforce evidence-based reasoning that requires 100/100 confidence before delivering a final result. If full certainty is unachievable, the system outputs a checklist detailing missing information required to reach certainty.

When an AI cannot reach 100 percent confidence for factual verification, it provides an explicit uncertainty note and outputs a concise checklist detailing exactly what missing information is required to reach full certainty.

You should use confidence gating for AI-generated content in legal, medical, security, or finance contexts where hallucinations are unacceptable. It ensures answers are only delivered when available evidence fully supports a 100/100 confidence level.

To get a checklist of missing information for AI uncertainty, apply strict confidence gating that withholds final answers until 100/100 confidence is met. The system then generates an explicit missing-information checklist automatically.

Yes, evidence-based AI reasoning works with project-specific rules by respecting skill-context overrides or evolution commands when present. This maintains strict confidence gating while adhering to customized project requirements.

Your AI outputs a missing-information checklist instead of a final answer because strict confidence gating prevents unreliable results. It withholds the final answer until evidence supports 100/100 confidence, explicitly acknowledging context uncertainty.