aif-grounded

To enforce evidence-based reasoning and prevent AI guessing, you need a strict reliability gate that classifies requests, verifies changeable facts, computes a 0–100 confidence score, and outputs either fully sourced answers or an INSUFFICIENT INFORMATION notice.

The best way to verify AI responses for security, finance, legal, or medical contexts is to implement a workflow that identifies supporting sources and unknowns, requiring 100% confidence before delivering an answer rather than allowing speculative outputs.

To calculate a 0–100 confidence score with explicit uncertainty, identify all available evidence and unknowns, verify changeable facts against sources, and only assign 100% confidence when the answer is fully supported, otherwise returning an INSUFFICIENT INFORMATION notice.

Yes, this approach works for AI safety scenarios with evolving facts by applying a strict workflow that verifies changeable facts and requires explicit listing of supporting sources before providing any high-stakes answer.

When there is insufficient information to reach 100% confidence, the system returns an INSUFFICIENT INFORMATION notice accompanied by a concise checklist of missing evidence and unknowns required to reach full confidence.

You should not use an evidence-based reliability gate when a task requires creative generation or speculative reasoning, as this approach strictly prevents guessing and only outputs answers supported by 100% confidence and verified sources.