AI product designGuide5 min

AI products need decision surfaces, not just chat boxes

Model output becomes useful when users can inspect the decision, change the inputs, and see what will happen next.

Real example

Example: replace a chat answer with a decision table

A hiring tool asks AI to rank candidates. The chat answer sounds impressive, but recruiters need to compare requirements, evidence, risks, and next steps.

Render each candidate as a row with must-have match, evidence, concerns, suggested interview focus, and reviewer decision. Keep chat for follow-up questions, not the main workflow.

The user can inspect and act on the recommendation instead of trusting a polished paragraph.

Tutorial path

How to implement it

Step 01

Turn the model output into fields, statuses, evidence, and actions.

Step 02

Show editable decision inputs before downstream work runs.

Step 03

Separate model suggestions from approved business state.

Step 04

Track which user accepted, changed, or rejected the suggestion.

Step 05

Feed review outcomes back into evaluation examples.

Checklist

Ready when these are true

Output is inspectable

Actions are explicit

Approved state is separate

Review history stored

Uncertainty is visible

Field notes

What matters in practice

Chat is useful for exploration, but production users need state, history, and repeatable actions.

Structured outputs should be visible enough for review before they trigger downstream work.

A good AI feature makes uncertainty explicit instead of hiding it behind confident copy.

Avoid these mistakes

Common failure modes

Do not hide important decision data in generated prose.

Do not skip editable fields.

Do not make the model's recommendation look like approved business state.

Practical tip

If the user has to copy text out of chat to do the work, the product surface is incomplete.

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