Preview output

Why does preview output matter?

This is a type of safety rail UX, built for when the cost of being wrong is too high; wasted credits, broken workflows, or just wasted time. It helps users see before they commit.

Most recently, people expect more transparency from AI tools. And, one of the ways to cater to building trust is giving them a glimpse of the output, instead of having them blindly run a long process and hoping for the best.

It’s the equivalent of a “test drive” before buying the car, a small peek that de-risks the big investment.

Time to test-drive this pattern.

Key takeaways

1. Show before you scale

Let users preview results on a single row, a snippet, or a sample before running on hundreds of entries.

Clay shows sample outputs in its tables. Users can quickly validate if the logic is working before spending credits across hundreds of rows.

2. Make it cheap to experiment

Position the preview as low-cost, low-stakes — “check first, then run”. This reduces fear of wasting credits.

Sometimes a preview is a quick sketch (instant inline status), other times it’s a full sample (multiple rows of generated data). The right level depends on stakes: higher cost → higher fidelity.

Clay leans high-fidelity with real row previews. Cursor leans lightweight with inline feedback. Both are valid ends of the spectrum.

3. Expose the edges

Preview should reveal possible errors, edge cases, or missing data, not only polished successes. Don’t sugarcoat the preview. Being honest/transparent builds more trust than a perfect but misleading mock.

4. The yes/no moment

A preview is useless without a decision.

Hex's “Accept” vs. “Reject” step crystal clear so users always know what happens next.

Clay nails this with its two buttons: “Output is wrong” vs. “Output is correct, save formula”. Users always know the next step.

5. Turn preview into a loop

Let users adjust prompts, constraints, or settings right from the preview instead of restarting the whole process.

Clay lets you re-edit the formula and re-run the preview instantly. And if the output is wrong, you can correct it yourself and make the system learn your intention. This becomes a closed learning loop.

Editor's notes

As a practitioner, I like to write notes — key takeaways and questions — to ask myself whenever I'm designing for preview in the future, ensuring a transparent “test‑drive” of AI before committing.

It’s a tangible gut-check for myself and for you to steal, if you see fit.

Future play

Today preview is about saving credits and catching mistakes because in the future, it can evolve into something far bigger: imagine a collaborative negotiation between human and AI before the “real” work happens.