Building an AI Coaching App in Expo with Local LLMs

Building an AI coaching app in Expo requires structured data collection, not just text bubbles. By using the WireAI SDK and a local model like Llama 3, you can generate native mood selectors and habit trackers dynamically, ensuring user privacy and a premium mobile experience.

A mental wellness app shouldn't feel like a terminal window. It's a clear use case for generative UI. You want the AI to present structured questions, log tappable responses, and summarize progress natively.

What is the ideal app flow?

Instead of a blank text input, let the app guide the user. The WireAI agent can render a `MoodSelectorCard` to start the session, follow up with a text input for journaling, and conclude with a `ProgressSummaryCard`.

How do you register custom coaching components?

Define your component and register its schema with WireAI.

import { registerComponent } from 'wireai-rn';
import { z } from 'zod';

export const MoodCheckIn = registerComponent({
  name: "MoodCheckInCard",
  description: "Use at the start of a coaching session to ask the user their mood.",
  schema: z.object({
    options: z.array(z.string()).describe("List of 4 mood options"),
  }),
  render: ({ props }) => <MoodUI options={props.options} />
});

How do you tune the system prompt?

WireAI automatically handles the JSON formatting instructions. You just need to provide the domain context. Pass a `systemPromptSuffix` like: "You are an empathetic coach. Never give long advice. Always ask one question at a time using the provided UI components."

Build better mobile workflows. Run npm install wireai-rn zod.