From MVP to Production: Hardening AI Products Without Killing Velocity
The staged approach we use to take AI features from prototype to dependable product — monitoring, fallbacks, and cost control included.
Prototype fast, harden deliberately
Speed matters early: validate that users want the AI feature before engineering it for scale. But set a hardening checkpoint — the moment usage becomes real, reliability work starts. Teams that skip the checkpoint pay for it in outages and trust.
The hardening checklist
Add timeouts, retries, and graceful degradation for every model call. Define fallback behaviour when the model is unavailable or unconfident. Introduce rate limiting and cost alerts — LLM bills scale with success, and an unmonitored feature can double your cloud spend in a week.
Quality as a moving target
Models drift, providers update, and user behaviour shifts. Continuous evaluation against a golden set, canary releases for prompt and model changes, and user feedback loops keep quality visible instead of anecdotal.
Team habits that scale
Treat prompts and evaluation sets as versioned code. Review AI behaviour changes like schema migrations. And keep one dashboard where product, engineering, and leadership see the same accuracy, latency, and cost numbers.
