How I move an AI demo toward production

Most AI demos fail at the handoff between "it works once" and "the team can own it." The real work starts when a promising interaction needs stable contracts, predictable failure modes, and enough observability to explain what happened in production.

The first change I usually make is to separate prompt experimentation from product behavior. A product team needs explicit request and response boundaries, even if the model behind them continues to evolve.

What changes after the prototype

• The prompt stops being the whole feature and becomes one part of a wider system.
• Fallback paths need to exist before the feature reaches real users.
• Logging and evaluation need to answer whether the model was useful, not just whether the API returned text.

A practical baseline

Start with typed inputs, typed outputs, and a narrow surface area. Add simple evaluation fixtures before building a large orchestration layer. That usually creates a better delivery path than trying to solve every future problem up front.