Deployment
Getting a working system in front of real users, reliably, and keeping it there.
Deployment is the part between a working demo and a running product: the model calls need credentials, the prompts need to be versioned, the failures need to be visible, and someone needs to know when quality drops.
For an AI system specifically, deployment means preparing for the failure modes that are normal in this technology. The provider will rate-limit you, the model will change behaviour, a prompt will eventually be found wrong in production, and a user will send something you did not anticipate. None of these are exceptions.
The minimum viable setup is unglamorous: structured logs with the model id, the prompt version, and the token counts; an evaluation run on a schedule; and a way to roll back a prompt change in one step. Everything else is refinement.
Related terms
Monitoring
Watching a running system so you learn about drift before your users complain.
Regression
A change that made something worse. The reason to keep tests at all.
Evaluation
Measuring whether the system does the job, on a fixed set of cases, repeatedly.
API
A defined way for one program to ask another for something.
Guardrails
Checks that stop or constrain an action before it happens.
Want this working inside your team?
It starts with one conversation about your workflow.