A reliable AI app needs a recoverable request, not only a good prompt. Validate the input, keep provider credentials on the backend, give each task a clear state and let the user return to the last confirmed result. Treat an uncertain external action as uncertain until you have evidence.
This guide is a design checklist for developers and technical buyers. It is not a claim that every safeguard below has been demonstrated by the promotional video.
Map the lifecycle before adding models
Start with a simple sequence: draft, submitted, processing, result ready, or needs attention. These are example states, not a required framework. Explain each state in words a user can understand.
An AI model interprets information. Ordinary application code should control the request ID, permissions, time limits and record updates. If the result will trigger an invoice, message or other external change, define a separate approval and execution boundary.
InterviewCoach provides a concrete mobile example to inspect: a short practice session and a review that the user can revisit. The public demo shows the product flow, not a complete audit of its internal architecture.
Protect the input and the secret
Check the input type and size before spending model tokens. Do not request data simply because a text field can accept it. For a CV-based tool, the user's employment history needs different handling from a public demonstration example.
Keep provider secrets out of the mobile bundle and website source. Record only the operational information needed to diagnose failures. Do not send CV text, personal answers or credentials to marketing analytics.
A mobile interface, your backend and an external model are different data boundaries. "Self-hosted app" is not the same statement as "all processing stays on this server."
Validate more than the JSON shape
A machine-readable response can be correctly formatted and still contain an unsupported conclusion. Validate required fields, permitted values and the relationship between them. When important information is missing, represent it as unknown or ask for clarification.
For a consequential workflow, identify the facts a person must confirm before the next action. Do not promote an AI confidence score into proof that a business decision is correct. Separate a useful draft from a completed action.
Make interruption a normal case
Test a slow request, an app restart, a temporary network failure and a second tap. The user should know whether work is still processing, has failed safely or has produced a confirmed result.
Android's state-saving guidance distinguishes UI state from long-term persistence. Restoring a text field does not automatically restore a confirmed backend result. Choose a durable record for the information the user must keep, and make its retention policy explicit.
For example, test whether a completed report can be reopened after the app closes. Do not substitute a passing build for that observed user journey.
Retry only when the operation permits it
If a read fails, a bounded retry may be reasonable. If an external write might already have succeeded, blindly repeating it can create a duplicate payment, message or record. First reconcile the request or use an idempotency mechanism actually supported by that destination.
The HTTP semantics standard describes idempotency and cautions against automatically retrying a non-idempotent request without knowing it is safe. An application request ID alone does not create that guarantee at a third-party service.
Set request, cost and retry limits. A fallback should clarify what happened and what the user can do, rather than pretending the model completed the task.
A small acceptance matrix
- Missing required input: show the correction before processing.
- Provider timeout: show an honest state and the permitted next action.
- Duplicate tap: inspect whether duplicate work is prevented or reconciled.
- Interrupted session: recover the last confirmed result.
- Unsupported answer: flag it instead of inventing evidence.
- Unknown external write: check status before any repeat.
Record the input, expected result, observed result and application version for each case. Keep real personal data out of shared demonstrations and public reports.
Connect the engineering to the buyer's decision
The goal is not a larger list of technologies. It is a workflow a person can trust and recover. Our AI app build guide explains how to put those requirements into a small first scope.
If your team needs help defining that scope, start with a free 15-minute Google Meet intro. Engineering and ongoing support are separately agreed paid services.