From Prototype to Production: Shipping AI Features

Dev team — you’ve built an AI prototype that works. The demo went great. Now someone wants it in production. This is where most AI projects die, and it’s not because the tech fails — it’s because nobody planned the gap between “works on my laptop” and “works for 1,000 users reliably.” Let’s fix that.

The Production Checklist:

1. Define “done” before you start. A prototype is done when it demonstrates the concept. A production feature is done when:

• It handles edge cases gracefully (not just the happy path)
• It has error handling for API failures, timeouts, and unexpected responses
• It has monitoring and alerting (you know when it breaks before users tell you)
• It has a rollback plan (you can turn it off without breaking everything else)

2. Manage latency expectations. AI API calls take time — typically 1-5 seconds for a substantive response. That’s fine for some UX patterns (chat interfaces, background processing) and unacceptable for others (autocomplete, real-time search). Design your UX around the latency reality:

• Stream responses token-by-token for chat interfaces
• Use async processing for batch operations
• Cache common responses when appropriate
• Set clear loading states so users know something is happening

3. Handle costs at scale. Your prototype made 100 API calls during testing. Production might make 10,000/day. Do the math before you ship:

• Track token usage per request type
• Set budget alerts and rate limits
• Use the cheapest model that meets quality requirements (Haiku for simple tasks, Sonnet for standard, Opus for complex)
• Cache identical or near-identical requests

4. Test with adversarial inputs. Users will do things you didn’t expect. They’ll paste 50,000 words into a field designed for 500. They’ll try prompt injection (Day 25). They’ll ask questions in languages you didn’t plan for. Build test cases for the weird stuff, not just the demo path.

5. Monitor output quality over time. AI outputs can drift as providers update models. What worked perfectly last month might behave differently after a model update. Set up quality monitoring:

• Log a sample of inputs and outputs
• Review a random sample weekly
• Have automated checks for obvious failures (empty responses, error messages in output, responses that are way too long or short)