Anticipate failure modes

For each severe overfitting failure on the spam classifier lab, define stop condition, safe state, owner, and lasting prevention. Rollback only works if prior prompts, indexes, and models remain available. “Send to a human” needs queue capacity and context—not just a button name.

Run one tabletop on the spam classifier lab for overfitting: inject a defect, verify detection, contain, recover, and keep the blameless trace.

After the tabletop, store the injected overfitting defect for the spam classifier lab as a regression fixture. If the same failure later reaches users silently, your detection story was aspirational. Detection without a fixture tends to rot for overfitting.

Also pin one numeric memory from this overfitting chapter: generalization gap = training score − validation score = 0.99 − 0.84 = 0.15 That number is not decoration; it is a template for how claims about overfitting on the spam classifier lab should look in design docs. Scoped specifically to overfitting / spam classifier lab / failure-modes.

People confuse overfitting with neighboring buzzwords when debugging the spam classifier lab. Before changing prompts, ask whether the broken stage was evidence gathering, the overfitting judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried overfitting and it failed”) that blocks the next team on the spam classifier lab. Scoped specifically to overfitting / spam classifier lab / failure-modes.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to overfitting playground rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to overfitting playground rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to overfitting playground rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to overfitting playground rather than generic AI advice.