Aglet

Learn from zero-query discovery and blank-state choices

A zero-query lesson should preserve what a person needed before typing and which guidance helped without narrowing their intent. Compare later first-use and returning journeys with the original state, then improve one review prompt about scope, history, examples, or recovery.

Keep the lesson for the next incident

  1. Keep the first state

    Record entry context, available history, scope, examples, focus, and first action. Note what was unknown. This separates a useful static cue from a personalized suggestion and keeps privacy assumptions out of the conclusion.

  2. Compare later starts

    Review new, returning, category-entry, and no-history paths after the change. Look for a context where an example misleads or a useful cue disappears. Preserve a minimal blank control so the lesson has a fair comparison.

  3. Refine one question

    Require future reviews to ask what the blank field covers, how a person starts, what data powers any suggestion, and how to return to literal input. Assign ownership and revisit date. Define the observation that would show the prompt reduced first-search hesitation.

What to carry forward

The learning record connects one blank-state choice to later first actions and a reusable review question. Stop when another reviewer can apply it without assuming personalized history or demand. Carry forward uncertainty where the value of an example remains context dependent.

Technical background: W3C guidance.

Keep the decision with the work.

Use a Work Item in Aglet to record the problem, the evidence you have, and the next decision. Add an owner and priority, then keep updates in the discussion so the next person can follow the reasoning.

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