Understand the mechanism

Define tasks and labels/rubrics, freeze versions, run the system, score with deterministic checks and/or human raters, compare against baselines, and watch for contamination.

Read the evals and benchmarks path as a pipeline for the coding copilot. At each stage, name the representation, the owner, and how information can be lost. Identifiers must mark prompt versions, model versions, indexes, and policies so “randomness” is not the default explanation for every bug in 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. Scoped specifically to evals and benchmarks / coding copilot / mechanism.

Keep the unit and the denominator visible when you discuss evals and benchmarks. A percentage without a base, or a latency without a percentile, hides the failure mode this chapter cares about on the coding copilot.

Learned stages estimate; deterministic stages enforce. A fluent result from the coding copilot does not prove evals and benchmarks used the right evidence. Preserve intermediates when privacy allows—candidate lists, traces, scores, citations—so you can see the first broken stage in the evals and benchmarks path.

Operational correctness for evals and benchmarks includes deadlines on the coding copilot. If stages that feed this workload sum past the user’s patience, trim earlier—usually pack less, retrieve less, or parallelize—before blaming the model vendor for evals and benchmarks. Mechanism diagrams that ignore time are incomplete.

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 / mechanism.

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 / mechanism.

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.