There’s a comfortable fiction in how extraction tools imagine their output being used: the user receives the structured result, reads each field carefully, cross-checks it against the source document, and confirms every value before relying on it. It’s a reassuring picture because it puts the final correctness check on the human. If something’s wrong, well, they were supposed to catch it. But it’s not what happens. Real users skim. They glance at the output, spot-check a thing or two that catches their eye, and move on. The thorough review the tool was counting on is a review that mostly doesn’t happen.

This isn’t laziness — it’s the entire reason they’re using the tool. A person who carefully verifies every extracted field against the source is doing the same cognitive work as extracting it by hand, which is exactly the work they were trying to avoid. The tool’s value proposition is not having to check everything. So the more useful the tool is, the less its output gets scrutinized, which means the tool’s own correctness matters more precisely when the human safety net is thinnest. Designing as if the user will catch your mistakes is designing against the grain of why they adopted you.

If the review is going to be a skim, then the output has to be built for skimming. That means the things most likely to be wrong, or most costly if wrong, should be the things that draw the eye — not buried in a uniform grid of values that all look equally settled. A field the tool is unsure about should look different from one it’s confident about. A value that’s unusual for its type should be visually flagged, not rendered identically to a mundane one. The goal is to spend the user’s limited attention where it actually changes outcomes, instead of letting it spread evenly across fields that don’t need it and run out before it reaches the one that does.

This reframes confidence information as an attention-routing tool rather than a disclaimer. A confidence score tucked in a tooltip nobody opens does nothing for a skimming user. But confidence expressed as visual weight — the uncertain fields surfaced, the shaky extraction pulled to the top of the review queue — turns the skim into something useful. You’re not asking the user to review everything more carefully; you’re using what the tool knows about its own uncertainty to aim the quick look they were always going to give. The tool participates in the review instead of passively hoping one happens.

The deeper principle is that you should design for the behavior users actually have, not the behavior that would make your tool safe. Wishing for a diligent reviewer doesn’t produce one. A tool that accepts the skim as the real interaction — and shapes its output so a five-second glance lands on the things that matter — gets more genuine safety than one that assumes a careful audit and quietly relies on it to cover for mistakes. Meet the user where their attention actually is. It’s shorter than you’d like, and the output should be built for exactly that.