Engineering / advanced
Evidence for model release decisions
Design evaluation datasets, scoring, uncertainty estimates, slices, and release gates that answer a stated deployment question rather than optimize a headline score.
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Module 01 advanced
Evidence for model release decisions
Design evaluation datasets, scoring, uncertainty estimates, slices, and release gates that answer a stated deployment question rather than optimize a headline score.
- 01
Define the population and quantity behind an evaluation score
Write an evaluation question as a population, system configuration, scoring rule, and estimand before choosing a dataset or release threshold.
24 min - 02
Construct a test that preserves the intended separation and sampling design
Build evaluation records from a documented population frame, keep related entities across split boundaries under control, and separate development feedback from final evidence.
24 min - 03
Collect enough support for the failures an aggregate can hide
Plan rare-slice coverage, distinguish prevalence estimation from challenge testing, and interpret zero observed failures using a fixed-sample probability model.
24 min - 04
Resample the independent units rather than their repeated rows
Identify conversation, user, and source dependence; preserve it in cluster resampling; and distinguish more bootstrap repetitions from more independent evidence.
24 min - 05
Compare models on matched cases and preserve the pairing
Estimate within-case improvements, distinguish effect size from a hypothesis test, and compute a fixed-sample exact discordance test under a stated null model.
24 min - 06
Treat the scoring process as part of the measurement system
Design operational rubrics, distinguish disagreement from missing labels and true error, and preserve individual judgments rather than presenting consensus as unquestionable ground truth.
24 min - 07
Validate automated judges against a protected human measurement process
Measure judge errors, position and style effects, confidence calibration, and slice behavior without treating a model-generated score as independent truth.
24 min - 08
Preserve test independence across training, retrieval, and feedback channels
Track known overlap, semantic near duplicates, benchmark feedback, and authorized access without mistaking an overlap search or sealed file for a complete independence guarantee.
24 min - 09
Make the release rule explicit across metrics, slices, and uncertainty
Use predeclared metric directions, operational thresholds, and simultaneous evidence budgets while distinguishing a release rule from statistical multiple-testing procedures.
24 min - 10
Preserve the evidence chain from eligible cases to the release decision
Bind model, data, prompts, scoring, raw outcomes, uncertainty, and approvals into immutable evaluation records that can be checked and rescored without rewriting history.
24 min