Shipping AI to Production

Shipping AI to Production

A prototype that works on your laptop is not a product. Production AI has to survive real users, real traffic, and real bills.

What to ship besides the prompt

• Caching for repeated queries
• Timeouts and retries with backoff
• Fallbacks — smaller model, search-only answer, or a human handoff
• Rate limits per user and per provider
• Prompt versioning so you can roll back
• Cost budgets so one runaway loop cannot empty the account

Failure is the default path

Assume:

• the provider will 500
• the model will be slow
• the user will paste a 20-page PDF

Then decide what the product does next.

A release checklist

1. 1

   Evals pass

   Golden-set scores are green — or you know exactly what regressed.

2. 2

   Traces are live

   You can inspect prompts, context, and outputs in production.

3. 3

   Kill switch exists

   Disable tools, switch models, or turn the feature off.

4. 4

   You can explain bad answers

   From logs, not from memory.

Keep going

This is the last lesson in the AI Engineer path. Return to the hub and use it as the map:

• AI Engineer Guide