ORBIOOM / AISet the right expectation
The product should explain its role before the first result appears. Is it drafting, searching, summarizing, recommending, or deciding? Clear framing helps people choose the right level of reliance.
Avoid language that implies infallibility. Confidence should come from evidence, useful controls, and consistent behavior—not from an interface that hides uncertainty.
Make sources and limits visible
When an answer depends on external information, show where that information came from and when it was retrieved. Source visibility allows users to verify important claims and understand the boundaries of the system’s knowledge.
Limitations should appear near the moment they matter. A generic disclaimer hidden in settings does not help someone evaluate a specific result.
Design for correction
Correction is not an edge case in AI products. People need fast ways to edit, retry, narrow, reject, and compare outputs. These actions should feel like normal parts of the workflow rather than recovery from failure.
Good correction tools also teach the system’s interaction model. When users can see how a changed instruction affects the result, they build more accurate expectations.
Protect user control and data
Users should know what information is being used, whether it is retained, and how to remove it. Privacy choices need plain language and sensible defaults, especially when documents, images, or personal work are involved.
Automation should remain interruptible. Let people review consequential actions, understand what will happen next, and reverse decisions when possible. Trust grows when the product demonstrates that the user remains in charge.
TakeawayTrustworthy AI design does not promise perfect intelligence. It creates clear expectations, visible evidence, strong correction paths, and meaningful user control.