The prompt instruction “if unsure, ask” doesn’t actually work.
Confidence scores repeat the same problem, since they are ultimately the AI’s own self-assessment.
Human instructions are inherently incomplete, so Unknowns arise frequently.
The lack of information itself is not the problem. The real problem is who fills in that gap — which is fundamentally a question of the allocation of Authority.
① Separation of Verification AI and Execution AI: The judgment of completeness is separated from execution.
② Two constraints placed on the Verification AI:
③ Reduction of the AI’s role: The AI’s role shrinks from subjective “Reasoning” to objective “Matching.”
④ Iterative feedback loop: As long as any Unknown remains, the AI repeatedly asks the user, and execution is withheld until every item is filled.
⑤ Enforcement through JSON-based coding:
This isn’t about making AI smarter — it’s about limiting the authority AI is allowed to exercise.
What is being changed is not the model, but the architecture.
The checklist is merely a policy layered on top of this architecture; the architecture itself is the structure of authority transfer and verification.
“Unknowns can arise at any time. But who fills them in is something we can design.”
The authority to fill in the blank is transferred from the AI to the human, and that transfer is enforced through the separation of verification and execution and through a structured state (such as JSON).
Depending on what is placed inside the checklist, this can extend into a variety of systems — compliance verification, accountability tracing, Human-in-the-Loop (HITL), regulatory compliance, and more.
In other words, this is not a technique that eliminates all reasoning errors. It is a practical safeguard that systematically enforces the points where human intervention is absolutely required.