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◆ AI Learning · Hard rule

Deliver Partial Certainty Instead of Hiding Complete Progress

When useful parts of the answer are already known, expose them rather than withholding everything until every uncertainty disappears.

By Pawan Nayar · from the LLOS AI Learning record

The rule

When useful parts of the answer are already known, expose them rather than withholding everything until every uncertainty disappears.

Separate established results from unresolved results. AI often treats delivery as binary: either everything is completely verified, or nothing should be shown. That can make useful work invisible for long periods. A stronger system distinguishes between: * verified, * provisionally supported, * unresolved, * and blocked. This allows the human to use finished work immediately while understanding where uncertainty remains. Partial delivery should never disguise uncertainty. Its value comes from making uncertainty explicit. **What is actually new here — the four labels.** Delivering early is already covered: the leash rule says ship one real item and get eyes on it before the full run, and the scale gate says the small batch IS the decision. What was missing is the vocabulary. Work was reported as done or not reported at all, with nothing in between, so "unresolved" and "provisionally supported" arrived disguised as one or the other. Those four words are the registered part of this card, and they are what makes Card 6 safe to use: cut the depth, then say plainly which label the result carries. **The earliest label is *unverified*, and it is delivered first.** Card 6 makes verification a switch the user holds. This card is how that switch gets offered: the first hand-over is the raw result marked unverified, before any checking has run — not a status update about work in progress, the actual usable thing. The four labels are the states it can move through afterwards, if the user chooses to pay for the move. A result held back because it is not yet verified is a result the user cannot decide about.

An AI agent is reviewing twenty generated learning components. After checking twelve, it has found the same layout defect in three and confirmed nine are correct. Instead of remaining silent until all twenty have been inspected, it can report the discovered pattern immediately. The user may recognize that the defect has a common cause and change the instruction, saving eight unnecessary verification cycles. Intermediate findings are not unfinished noise. Sometimes they change the optimal next action.

Words you'll need

Resolved from this lesson's own record — real definitions, not invented for this page.

Human-In-The-Loop

A person checks the AI's work before it matters

Questions people ask

More from the same record, pulled by shared topic — not written new for this page.

How should a developer talk about their own mistakes?

A developer must use precise words when talking about their own failures. The developer should avoid polite words that make a mistake seem less serious.

When should an AI model show the full paths of files it has changed?

An AI model must show full file paths after every major update. A major update is any change that modifies one or more files.

What is a poll in an AI system?

A poll is a type of interaction. It is built directly into the AI system.

How does a person know if they are truly learning and improving, or just moving from one task to another?

A person learns best when they know their own weaknesses. This is called self-awareness.

What to remember

When useful parts of the answer are already known, expose them rather than withholding everything until every uncertainty disappears.