Evaluate with evidence

Track for evals and benchmarks: pass rate with CI, slice gaps, scorer agreement, contamination indicators, cost per accepted task. Report fractions like 36/40, not vague quality adjectives. Segment by language, plan tier, document length, or other slices that matter for the coding copilot.

Freeze inputs and neighboring versions while evaluating evals and benchmarks. Change one control. Pair results case by case on the coding copilot. Define hard gates (severe errors, privacy, latency) before the bake-off. Use deterministic checks where possible; humans for nuance; model judges only with calibration against gold.

Numeric reminder for evals and benchmarks: 80/100 pass on unit-test tasks with 95% CI roughly ±4 pts—do not call a +2 pt vendor demo a revolution.

Resist adding a twelfth metric before the first three for evals and benchmarks on the coding copilot have owners. This workload improves faster when a small scorecard is trusted than when a warehouse of unused plots exists.

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 / evaluation.

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 / evaluation.

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.

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.