aif-grounded

To prevent AI guessing and ensure evidence-based answers, a grounded reasoning workflow classifies requests, lists evidence and unknowns, verifies changeable facts, and applies a 0–100 confidence gate to output a 100/100 answer or an INSUFFICIENT INFORMATION response.

A confidence gate works by enforcing a 0–100 scale evaluation for verified outputs. If evidence meets the 100/100 threshold, the system provides a grounded answer; otherwise, it explicitly returns an INSUFFICIENT INFORMATION response instead of guessing.

You need grounded reasoning for AI safety in medical, legal, or financial domains when stakes are high and context may be incomplete. It forces explicit uncertainty, mandatory fact verification, and evidence-based conclusions rather than hallucinated outputs.

Yes, you can force an LLM to return insufficient information instead of hallucinating by applying a strict confidence gate. This workflow requires listing unknowns and verifying changeable facts, blocking any output that does not achieve 100/100 confidence.

The step-by-step process for evidence-based conclusions involves classifying the request, defining evidence and unknowns, performing mandatory verification for changeable facts, applying a confidence gate, and formatting the output as either a verified answer or an insufficient information prompt.

A limitation of a strict 100/100 confidence gate for AI outputs is that it will frequently return an INSUFFICIENT INFORMATION response when context is incomplete, intentionally blocking answers in high-stakes domains where verified facts are unavailable.