Building AI-Powered Apps: Lessons Learned

After shipping three AI-powered mobile apps, here’s what I wish I knew from the start about integrating LLMs into production applications.

1. Start with the UX, Not the Model

It’s tempting to focus on the AI capabilities first, but the real challenge is designing an experience that feels natural and helpful. Users don’t care about your model architecture—they care about whether the app solves their problem.

2. Latency is Everything

Mobile users expect instant responses. A 3-second delay that feels acceptable in a web browser feels like an eternity in a mobile app. Consider:

• On-device inference for simple tasks
• Streaming responses for longer outputs
• Optimistic UI patterns to mask latency

3. Plan for Failures

AI models will sometimes produce unexpected outputs. Build robust fallbacks:

• Validate model outputs before displaying
• Have graceful degradation paths
• Always give users a way to correct or override AI decisions

4. Cost Management

API costs can spiral quickly if you’re not careful:

• Cache responses aggressively
• Use smaller models for simple tasks
• Implement rate limiting and usage caps

The best AI features are often invisible—they enhance the experience without drawing attention to themselves.