Case context: A team has built an automatic speech recognition model, and it is making many mistakes even on the examples it was trained on, not just on new evaluation data.

Question: What should the team do to figure out why the model is struggling on the training data, and what should they generate from that review?

Sample answer: They should examine about 100 training examples that the model handles badly and inspect them by hand. As they review them, they should sort the mistakes into categories and count how often each category appears. The result should be a list of the main types of training-set errors, which helps the team decide what problem to address first.

Key points:

• Inspect roughly 100 badly handled training examples
• Group the mistakes into categories
• Count how often each category appears
• Use the summary to prioritize fixes, similar to development-set error analysis

Rubric: Full credit for identifying the review of about 100 bad training examples, the categorization and counting of errors, and the comparison to development-set error analysis.