Anticipate failure modes

For each severe evals and benchmarks failure on the coding copilot, 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 coding copilot for evals and benchmarks: inject a defect, verify detection, contain, recover, and keep the blameless trace.

After the tabletop, store the injected evals and benchmarks defect for the coding copilot 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 evals and benchmarks.

Also pin one numeric memory from this evals and benchmarks chapter: 80/100 pass on unit-test tasks with 95% CI roughly ±4 pts—do not call a +2 pt vendor demo a revolution. That number is not decoration; it is a template for how claims about evals and benchmarks on the coding copilot should look in design docs. Scoped specifically to evals and benchmarks / coding copilot / failure-modes.

People confuse evals and benchmarks with neighboring buzzwords when debugging the coding copilot. Before changing prompts, ask whether the broken stage was evidence gathering, the evals and benchmarks judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried evals and benchmarks and it failed”) that blocks the next team on the coding copilot. Scoped specifically to evals and benchmarks / coding copilot / 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 eval and benchmarks 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 eval and benchmarks 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 eval and benchmarks rather than generic AI advice.