Finding Coverage Gaps Before Review
Use this playbook when benchmark evidence looks plausible overall but the team needs to know whether important behavior slices are missing or underrepresented.
Review-readiness trigger
Use this before a customer relies on Benchmark evidence for human review, especially when results are dominated by common Cases, a critical Rubric has few applicable Cases, or specialists identify behavior that the Dataset does not represent.
Coverage audit
- Confirm the exact Benchmark Version and Run completeness before interpreting its distribution.
- Open Benchmark Datasets → Representation and inspect the selected Dataset across the configured Coverage Facets.
- Compare thin or empty slices with Coverage Stories, failed Cases, specialist observations, and must-level Rubrics with few applicable Cases.
- Classify each issue: missing vocabulary, missing Case, unreviewed candidate, missing Dataset membership, or unclear correctness standard.
- Route vocabulary changes to Coverage Facets, unclear standards to Expert Contributions, and missing Cases to Assets Synthesis or Case Foundry.
- Review candidates in Case Review, select the intended Cases in Benchmark Datasets, and create a new Snapshot.
- Run the relevant saved Harness Versions against the new Benchmark Version and state any still-unrepresented risk in the customer's review context.
Evidence threshold
Coverage is ready when critical slices are named, their selected Case counts are visible, candidate-only Cases are not counted as evidence, and remaining gaps are explicit. A balanced-looking aggregate count is not sufficient.
Worked example
Compatibility gaps
Dataset Representation shows many ordinary recommendations but only two accessory-compatibility Cases and no discontinued-model Cases. A Coverage Story records the gap. Case Foundry prepares cross-brand adapter, ambiguous model-number, and discontinued-model candidates; specialists review the source conditions in Case Review. Only the admitted Cases enter the new Snapshot, and the customer withholds compatibility conclusions until the rerun is complete.
Evidence to collect
- Benchmark Version, Snapshot, selected Case count, and Dataset Representation.
- Coverage Stories and specialist observations that identify consequential gaps.
- Candidate lineage, Case Review decisions, and final Dataset membership.
- New Snapshot and Benchmark Version after membership changes.
- Rerun completeness and an explicit list of remaining unsupported conclusions.
Related docs
Source confidence
Doctrine-backed: the approved product model requires deliberate coverage before Benchmark evidence is trusted. Linked code-backed pages define current Representation, Coverage Story, Case Review, Snapshot, and Run boundaries.