How I structure AI flows in React Native apps

Most AI mobile apps look simple from the outside, but the real work is in the flow. If the user taps a button and waits for a random blob of text, the product will feel unreliable.

The pattern I use is straightforward:

1. capture the input
2. send it to a backend action
3. force a structured result
4. validate it
5. show an editable preview
6. save only after the user confirms

1. Capture the input cleanly

The app should know what kind of input it is receiving. A meal note, a voice recording and a screenshot are different flows. They should not all go through the same vague “generate” endpoint.

type AIInput =
  | { type: "meal_note"; text: string }
  | { type: "voice_answer"; audioUrl: string }
  | { type: "recipe_image"; imageUrl: string }
  | { type: "recipe_text"; text: string };

This makes the backend easier to reason about and gives the UI better loading states.

2. Move AI work to the backend

I avoid calling AI APIs directly from the mobile app. The client should not own API keys, prompt logic, model routing, retry behavior or schema validation.

The mobile app should send a clean request. The backend should decide the model, prompt, schema, retries and fallback behavior.

3. Always return product data, not prose

If I need a meal entry, I return a meal entry. If I need feedback, I return feedback fields. If I need a recipe, I return ingredients, steps and nutrition estimates.

type SpeakingFeedback = {
  summary: string;
  scores: {
    clarity: number;
    confidence: number;
    conciseness: number;
  };
  rewrittenAnswer: string;
  nextImprovement: string;
};

This keeps the UI stable. It also means I can store the result, show history, build analytics and improve the product later.

4. Show an editable preview

This is important. The model can be good and still be wrong.

For nutrition tracking, the user should be able to adjust quantities. For recipe parsing, the user should be able to edit ingredients. For speaking feedback, the user should see the transcript and fix it if transcription was wrong.

The product should not pretend the model is perfect. It should make correction easy.

5. Track usage and failure states

AI features can become expensive fast, especially image or audio flows. I track:

• how often users run AI actions
• which flows fail
• how often users edit the result
• which actions lead to saving
• where users hit free limits

That data matters more than “the AI response looked good in testing.” A real AI feature works when users come back and trust the result enough to save it.