From prototype to production: shipping a reliable AI feature
The distance between an impressive AI demo and a dependable feature is mostly invisible. The demo handles the happy path in front of an audience; production handles the messy 20% — odd inputs, missing data, the model having an off day — for real users who will not give you a second chance.
Evaluate before you ship, and after
Build an evaluation set of representative inputs with expected outcomes, and run it on every prompt or model change. Without evals you are guessing whether a tweak helped or quietly broke three other cases. Evals turn "it feels better" into a number you can trust.
Guardrails and graceful failure
Constrain what the model can do and plan for when it gets things wrong. Validate outputs against a schema, keep actions read-only or reversible where you can, and always have a sensible fallback — a clear "I’m not sure" beats a confident mistake.
- Validate and constrain every model output.
- Fall back gracefully instead of failing loudly.
- Rate-limit and budget so cost can’t spiral.
- Keep risky actions reversible or human-approved.
Observe everything in production
Log prompts, responses, latency, cost and user feedback from day one. When something goes wrong — and it will — you need to see exactly what the model saw. Observability is also how you find the next round of improvements: the real questions users ask are your best roadmap.
This checklist — evals, guardrails, graceful failure, observability — is how we take AI features from a working prototype to something you can put in front of customers. If you have a promising prototype that needs to become production-ready, that is our favourite kind of project.