---
id: playbooks.coverage-gaps-before-review
title: Finding Coverage Gaps Before Review
summary: Use coverage dimensions, failures, and expert signals to decide where evidence is incomplete.
kind: recipe
product_area: playbooks
status: stable
updated: 2026-08-23
canonical: /docs/playbooks/finding-coverage-gaps-before-review
---

# 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

1. Confirm the exact Benchmark Version and Run completeness before interpreting its distribution.
2. Open **Benchmark Datasets → Representation** and inspect the selected Dataset across the configured Coverage Facets.
3. Compare thin or empty slices with Coverage Stories, failed Cases, specialist observations, and must-level Rubrics with few applicable Cases.
4. Classify each issue: missing vocabulary, missing Case, unreviewed candidate, missing Dataset membership, or unclear correctness standard.
5. Route vocabulary changes to Coverage Facets, unclear standards to Expert Contributions, and missing Cases to Assets Synthesis or Case Foundry.
6. Review candidates in **Case Review**, select the intended Cases in **Benchmark Datasets**, and create a new Snapshot.
7. 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.

{% example-demo title="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.
{% /example-demo %}

## 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

{% related-card-grid title="Related docs" %}
- [Inspect Dataset representation](/docs/benchmark-datasets/representation)
- [Work with Coverage Stories](/docs/coverage-management/coverage-stories)
- [Review prepared Cases](/docs/coverage-management/case-review)
- [Read run results](/docs/benchmark-evaluations/inspect-results)
- [Run a benchmark](/docs/benchmark-evaluations/run-evaluation)
- [Importing cases](/docs/operating-manual/import-and-prepare-cases)
{% /related-card-grid %}

## 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.