Question: A team is tuning a model for document classification. Explain why a development set often contains somewhere between 1,000 and 10,000 examples, and why a set near 10,000 examples is much better for spotting a tiny change such as a 0.1% accuracy gain.

Sample answer: Development sets are often kept in the 1,000-to-10,000-example range because that range is large enough to give a usable signal without making evaluation too expensive. The best size depends on how small an improvement the team wants to measure.

If the team only cares about large changes, a smaller dev set may be enough. But if the goal is to notice a very small improvement, the set must be large enough that random variation does not hide the effect. With 10,000 examples, a 0.1% change corresponds to about 10 examples, which is much easier to distinguish from noise than in a 1,000-example set, where 0.1% is only about 1 example.

Key points:

• Dev sets commonly fall between 1,000 and 10,000 examples.
• Larger dev sets reduce noise when comparing close model versions.
• Around 10,000 examples gives a practical chance of detecting a 0.1% improvement.

Rubric: The response must state that development sets are often in the 1,000 to 10,000 example range. It must also explain that a larger set, especially one near 10,000 examples, is useful for detecting very small performance gains such as 0.1%, because smaller sets make such changes hard to separate from sampling noise and variance.