# Module 4 Narration

## Opening

Open with the professional setting: an engineering team converting prototype AI code into a maintainable application component. Ask students what decision is being made, who is affected, and what evidence would be persuasive to a skeptical reviewer.

## Middle

Move through the module in four passes:

1. Define **Testing and continuous integration** in the context of Applied AI Programming with Python.
2. Walk through the lab as a proxy-data exercise, emphasizing what it can and cannot show.
3. Compare a baseline with an AI-enabled or more sophisticated alternative.
4. Translate the result into stakeholder language: recommendation, risk, mitigation, and next evidence.

## Closing

Close by returning to the module artifact: **tested Python AI component with interface contract, CI evidence, and deployment notes focused on testing and continuous integration: Add unit and integration tests for a model pipeline.**. Students should leave knowing exactly what artifact they are producing and how it will be judged.