Prompt Strategies for Vibecoding Mobile Apps

Generic prompting advice — be specific, give context, iterate — applies to mobile too. But mobile has failure modes the web doesn't, and a handful of targeted techniques prevent most of them. This guide is the mobile-specific layer on top of the fundamentals.

If you haven't read Prompt Engineering for Code, start there. This assumes you know the basics.

Pin your stack, including versions

The single biggest source of broken mobile code is version drift. React Native, Expo, and the navigation libraries change APIs across major versions, and the AI's training data blends several eras together. Nail it down in every session:

Stack: Expo SDK 52, React Native, TypeScript, Expo Router (not React Navigation directly). Use function components and hooks. Prefer Expo packages over bare community packages.

Being explicit about Expo Router vs. React Navigation matters especially — they're different APIs for the same job, and the AI will happily mix them if you don't choose. Pinning the SDK version steers it toward current APIs.

Build one screen per prompt

Resist the urge to ask for the whole app. "Build me a fitness tracker" produces a wall of interdependent code you can't review or debug. Instead:

Build only the Home screen. It shows the current streak as a large number and a "Log workout" button that opens the log modal. Hardcode the streak as 5 for now.

One screen per prompt gives you code you can actually read, run, and verify before moving on. When it works, prompt the next screen. This is the vertical-slice approach from the planning guide, applied to prompting.

Give it the navigation structure explicitly

The AI can't see your app's shape. Hand it the map every time navigation is involved:

This app uses Expo Router with a tab layout: Home, History, Settings. "Log workout" is a modal route. Wire the Home button to open the Log workout modal and pass no params.

Without this, the AI invents a navigation structure — usually a different one each time — and you spend the session reconciling them. With it, every screen slots into the same skeleton.

Name both platforms, always

The default AI output often assumes one platform. Force it to consider both:

Handle iOS and Android: respect safe-area insets on both, make the form scroll so the keyboard doesn't cover inputs, and handle the Android hardware back button to close the modal.

Specific differences to call out when they're relevant:

• Safe areas — notch and home indicator on iOS, cutouts on Android.
• Back button — Android has a hardware/gesture back; iOS doesn't.
• Keyboard avoidance — inputs must stay visible when the keyboard opens.
• Date/time pickers, action sheets — look and behave differently per platform.

Handle native features by naming the package

When you need a device capability, don't ask the AI to "add camera support" and hope. Tell it which package and confirm it's Expo-compatible:

Add photo capture using expo-camera. Request camera permission with a clear rationale string, show a fallback if permission is denied, and save the photo URI to state.

Two guardrails here:

1. Prefer Expo/first-party packages. They're config-plugin based, so no native code editing, and the AI handles them well.
2. Ask the AI to include the permission flow, not just the happy path. Denied-permission handling is where store reviewers and real users trip you up.

If the AI reaches for a package you've never heard of, ask it to justify the choice and check the package is maintained before you install.

Be explicit about state and persistence

Mobile state has a wrinkle the web mostly ignores: the app gets backgrounded and killed. Decide and state where data lives:

Persist workouts to SQLite via expo-sqlite. On app launch, load them into state. This must work offline — no network calls for reading or writing workouts.

For simple key-value needs, name AsyncStorage. For structured data, name expo-sqlite. Saying "store the data" without specifying leaves the AI to pick, and it often picks in-memory state that vanishes when the app closes.

Ask for the loading, empty, and error states

AI-generated screens default to the case where data exists and everything works. Real screens need three more:

Include a loading state while workouts load, an empty state with a prompt to log the first workout, and an error state if the database read fails.

One sentence, and your screen stops looking half-finished the moment real conditions hit it.

When something breaks, give it the real error

Mobile errors are often build-time or native, and they're verbose. Paste the whole thing — the red screen, the Metro bundler output, or the EAS build log:

This EAS build failed. Here's the full log: [paste]. What's the cause and the fix?

Don't summarize the error. The AI reads stack traces and native build logs well, and the detail you'd trim is usually the detail that identifies the cause.

A prompt template that works

Combine the techniques into a reusable shape:

Stack: Expo SDK 52, TypeScript, Expo Router. Function components + hooks.

Context: [what exists / relevant part of SPEC.md]

Build: [one screen or one feature]

Requirements:
- Navigation: [how it connects]
- Data: [where it's stored, offline behavior]
- Platforms: handle iOS + Android [specific differences]
- States: loading, empty, error
- Permissions: [if any, include denied handling]

Fill it in, send it, review the one screen it produces, then move to the next. Precise and incremental beats ambitious and tangled every time on mobile.

Related

• Plan first: Planning a Mobile App Before You Prompt
• Put it into practice: Vibecoding an iOS + Android App with Expo