aif-grounded

To enforce evidence-based reasoning for high-stakes AI responses, apply a reliability gate that requires a 100/100 confidence threshold before delivering final answers, returning an insufficient information signal instead of guesses.

When AI confidence falls short in policy-driven tasks, the reliability gate surfaces an explicit 'what's missing' checklist, identifying specific information gaps needed to reach 100% confidence rather than outputting unverified results.

Yes, you can override general behavior by reading optional project-specific rules from skill-context, allowing you to tailor the reliability gate's evidence requirements to match your domain's exact policy-driven constraints.

You need a reliability gate for AI outputs in high-stakes, version-sensitive, or policy-driven scenarios where responses must be 100% confident and supported by evidence, preventing unverified guesses in critical domains.

Yes, grounded prompting works for security and legal-adjacent domains by enforcing strict evidence-based reasoning and surfacing explicit uncertainty, ensuring safe handling of high-stakes outputs that cannot tolerate hallucinated information.

The best way to prevent AI hallucinations in version-sensitive tasks is implementing a reliability gate that enforces a 100/100 confidence requirement, blocking final answers unless they are fully supported by evidence.