Building AI-Powered Apps: Best Practices for 2026

AI is no longer experimental — it's a core part of modern applications. But building reliable, cost-effective AI features requires careful planning. Here are our top recommendations.

1. Design for Failure

AI services can fail. Models go down, rate limits get hit, and responses can be unpredictable. Build resilience into your architecture:

try {
  const result = await abstrakt.run(model, input);
} catch (error) {
  if (error.code === 'RATE_LIMITED') {
    // Implement exponential backoff
  } else if (error.code === 'MODEL_UNAVAILABLE') {
    // Fall back to alternative model
  }
}

2. Implement Caching

Many AI requests are repetitive. Cache responses to reduce costs and latency:

3. Monitor Everything

Track key metrics to understand your AI usage:

4. Optimize Costs

AI can get expensive quickly. Control costs with:

5. Handle Content Moderation

AI can generate inappropriate content. Implement safeguards:

Conclusion

Building with AI is exciting but requires discipline. Follow these practices to ship reliable, cost-effective AI features that delight your users.