Development & Integration

Transform your architecture and data strategy into a working product with excellent user experience.

Start with API integration: If using third-party AI (OpenAI, Anthropic), begin with simple API calls before building complex features
Implement error handling early: AI responses can fail—handle timeouts, rate limits, malformed responses gracefully
Build feedback mechanisms: Allow users to rate, correct, or regenerate AI outputs
Optimize prompts iteratively: Test different prompts, track success rates, refine based on results
Cache aggressively: Store and reuse identical or similar AI responses to reduce costs
Use streaming when possible: Stream responses for text generation to improve perceived performance
Version your prompts: Track prompt changes like code—use version control, A/B test variants

AI features built directly into your application UI. Best for core product functionality.

Expose AI capabilities via API for developers to integrate. Great for B2B, platforms, and extensibility.

Add AI to existing tools (Figma plugins, Chrome extensions, Slack bots). Meets users where they work.

Connect to Zapier, Make, n8n for no-code integration. Expands reach with minimal effort.

Loading states: Show progress for AI tasks—users are more patient when they see activity
Set expectations: Communicate that AI isn't perfect, may require iteration
Provide examples: Show sample inputs/outputs to guide user behavior
Enable iteration: Make it easy to refine, regenerate, or edit AI outputs
Explain outputs: When possible, show reasoning or confidence scores
Handle errors gracefully: Clear error messages with suggested next steps

Key Takeaways

Start with simple API integration before building complex features
Build robust error handling and fallback mechanisms early
Optimize UX for AI uncertainty—show progress, enable iteration, set expectations
Cache responses and version prompts to control costs and improve consistency