Case context: A product team is experimenting with several neural network designs for a text classification system. They have not yet agreed on a standard validation split or on one score that will be used to judge each experiment. As a result, every meeting turns into a debate about which model is best.

Question: What should the team define first to make experimentation more efficient, and how does that help their work?

Sample answer: They should first create an initial development and test split and choose one evaluation metric to use for every trial. That gives them a consistent standard for comparison, so they can make decisions quickly and spend less time arguing over results.

Key points:

• There is no shared, objective way to compare models yet.
• The team should set up a development set, a test set, and one metric.
• A consistent metric lets the team compare experiments directly and move faster.

Rubric: The answer must state that the team should establish the development/test data split and a metric, and explain that this will speed up iteration.