The move from demo to production is not an engineering footnote. It is where real data, edge cases, cost, permissions, people, and accountability enter the picture. Ignoring it makes even impressive prototypes fragile.
Quality must be defined first
“Looks good” is not a metric. Build a representative test set, agree on what counts as an error, and set an acceptable threshold for each output type. Without evaluation, you cannot tell whether a system improves or degrades over time.
The workflow matters as much as the model
Who sees the result? What happens in uncertain cases? Where is human correction captured? How do you handle changed source material, out-of-scope requests, or provider downtime? Those questions define the real product.
Observability and cost are not optional
An operational system records the minimum needed to understand quality, latency, volume, failures, and cost per task while avoiding unnecessary sensitive retention. Without this visibility, you cannot govern spend or intervene before a problem becomes serious.
Taking AI to production means designing for the moment when it is not sure of the answer.
Assign ownership
Every system needs a business owner, a technical owner, and a process for updating data, prompts, models, and policy. Without those, a demo remains a side project even when the technology works.