Before you put an AI feature in front of users, run it through the checks that separate a demo from a system: evaluation, integration, observability, cost, and safe failure.
“It works” is not the same as “it’s ready.” An AI feature that looks great in a review can still be missing everything it needs to survive real traffic. Before you ship, it helps to have a concrete checklist — not a vibe, but a list of questions with yes/no answers.
Here’s the one we run before we call an AI system production-ready.
Evaluation: can you measure quality?
If you can’t score the output, you’re shipping blind. Before launch you want:
- A golden set of representative inputs with known-good answers or rubrics.
- Automated scoring that runs on every change, not a manual spot-check.
- A quality baseline you’ve agreed is good enough to ship — and a threshold that blocks regressions.
Integration: does it live inside the real system?
A model that runs in a notebook is a prototype. A production feature has to sit inside your actual product, data, and auth:
- Real authentication and authorization on every call.
- Inputs that come from production data, with its mess and edge cases.
- Outputs that downstream systems can consume — typed and validated, not free-form text someone has to parse by hand.
Observability: can you see what it did?
When a user reports a bad result, you should be able to answer “what happened?” in minutes, not days:
- Structured logs of what the system retrieved, decided, and returned.
- Traces you can replay for a single request.
- Dashboards for latency, error rate, and per-feature cost.
Cost and latency: does it hold up at scale?
A feature that’s great for one user can be a margin problem for ten thousand:
- A known cost per request, and a budget that triggers an alert.
- Latency measured under realistic load, not a single happy-path call.
- A plan for caching, batching, or routing to cheaper models where quality allows.
Failure handling: what happens when it breaks?
It will break. Production-ready means it breaks safely:
- Timeouts, retries, and fallbacks for every external call.
- Graceful degradation — a useful default when the model is slow or unavailable.
- Guardrails on inputs and outputs so a bad response can’t do damage.
Treat the checklist as a gate, not a wish list
The point of a checklist is that you don’t ship until the answers are yes. Most teams know these items exist; the discipline is refusing to launch until each one is actually true. That’s also why we front-load the hard parts — evaluation, integration, and cost — from the first sprint rather than bolting them on after a demo gets attention. The features that reach production are the ones that were built to pass this list from day one.
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