{"query":"Finding Coverage Gaps Before Review","corpusVersion":"local","generatedAt":"2026-09-13T04:40:37.538Z","results":[{"blockId":"playbooks.coverage-gaps-before-review#finding-coverage-gaps-before-review","pageId":"playbooks.coverage-gaps-before-review","title":"Finding Coverage Gaps Before Review","pageTitle":"Finding Coverage Gaps Before Review","url":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","humanUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review#finding-coverage-gaps-before-review","markdownUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","sectionId":"finding-coverage-gaps-before-review","kind":"recipe","productArea":"playbooks","score":4816.2608475916495,"reasons":["search_match","title_match","display_title_match","term_match"],"markdown":"# Finding Coverage Gaps Before Review\n\nUse this playbook when benchmark evidence looks plausible overall but the team needs to know whether important behavior slices are missing or underrepresented."},{"blockId":"playbooks.coverage-gaps-before-review#review-readiness-trigger","pageId":"playbooks.coverage-gaps-before-review","title":"Review-readiness trigger","pageTitle":"Finding Coverage Gaps Before Review","url":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","humanUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review#review-readiness-trigger","markdownUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","sectionId":"review-readiness-trigger","kind":"recipe","productArea":"playbooks","score":2274.4657911233294,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Review-readiness trigger\n\nUse 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."},{"blockId":"playbooks.coverage-gaps-before-review#coverage-audit","pageId":"playbooks.coverage-gaps-before-review","title":"Coverage audit","pageTitle":"Finding Coverage Gaps Before Review","url":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","humanUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review#coverage-audit","markdownUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","sectionId":"coverage-audit","kind":"recipe","productArea":"playbooks","score":2243.5477851378773,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Coverage audit\n\n1. Confirm the exact Benchmark Version and Run completeness before interpreting its distribution.\n2. Open **Benchmark Datasets → Representation** and inspect the selected Dataset across the configured Coverage Facets.\n3. Compare thin or empty slices with Coverage Stories, failed Cases, specialist observations, and must-level Rubrics with few applicable Cases.\n4. Classify each issue: missing vocabulary, missing Case, unreviewed candidate, missing Dataset membership, or unclear correctness standard.\n5. Route vocabulary changes to Coverage Facets, unclear standards to Expert Contributions, and missing Cases to Assets Synthesis or Case Foundry.\n6. Review candidates in **Case Review**, select the intended Cases in **Benchmark Datasets**, and create a new Snapshot.\n7. Run the relevant saved Harness Versions against the new Benchmark Version and state any still-unrepresented risk in the customer's review context."},{"blockId":"playbooks.coverage-gaps-before-review#evidence-threshold","pageId":"playbooks.coverage-gaps-before-review","title":"Evidence threshold","pageTitle":"Finding Coverage Gaps Before Review","url":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","humanUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review#evidence-threshold","markdownUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","sectionId":"evidence-threshold","kind":"recipe","productArea":"playbooks","score":1585.9190500048314,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Evidence threshold\n\nCoverage 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.\n\n{% example-demo title=\"Compatibility gaps\" %}\nDataset 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.\n{% /example-demo %}"},{"blockId":"playbooks.coverage-gaps-before-review#evidence-to-collect","pageId":"playbooks.coverage-gaps-before-review","title":"Evidence to collect","pageTitle":"Finding Coverage Gaps Before Review","url":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","humanUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review#evidence-to-collect","markdownUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","sectionId":"evidence-to-collect","kind":"recipe","productArea":"playbooks","score":1581.5141870435732,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Evidence to collect\n\n- Benchmark Version, Snapshot, selected Case count, and Dataset Representation.\n- Coverage Stories and specialist observations that identify consequential gaps.\n- Candidate lineage, Case Review decisions, and final Dataset membership.\n- New Snapshot and Benchmark Version after membership changes.\n- Rerun completeness and an explicit list of remaining unsupported conclusions."},{"blockId":"playbooks.coverage-gaps-before-review#source-confidence","pageId":"playbooks.coverage-gaps-before-review","title":"Source confidence","pageTitle":"Finding Coverage Gaps Before Review","url":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","humanUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review#source-confidence","markdownUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","sectionId":"source-confidence","kind":"recipe","productArea":"playbooks","score":1567.9484080760824,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Source confidence\n\nDoctrine-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."},{"blockId":"playbooks.coverage-gaps-before-review#related-docs","pageId":"playbooks.coverage-gaps-before-review","title":"Related docs","pageTitle":"Finding Coverage Gaps Before Review","url":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","humanUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review#related-docs","markdownUrl":"https://teammately.ai/docs/playbooks/finding-coverage-gaps-before-review.md","sectionId":"related-docs","kind":"recipe","productArea":"playbooks","score":1551.4367079831916,"reasons":["search_match","page_title_match","term_match"],"markdown":"## Related docs\n\n{% related-card-grid title=\"Related docs\" %}\n- [Inspect Dataset representation](/docs/benchmark-datasets/representation)\n- [Work with Coverage Stories](/docs/coverage-management/coverage-stories)\n- [Review prepared Cases](/docs/coverage-management/case-review)\n- [Read run results](/docs/benchmark-evaluations/inspect-results)\n- [Run a benchmark](/docs/benchmark-evaluations/run-evaluation)\n- [Importing cases](/docs/operating-manual/import-and-prepare-cases)\n{% /related-card-grid %}"},{"blockId":"coverage.coverage-gaps#task-steps-coverage-gaps","pageId":"coverage.coverage-gaps","title":"Task steps: Coverage Gaps","pageTitle":"Coverage Gaps","url":"https://teammately.ai/docs/coverage-engineering/coverage-gaps.md","humanUrl":"https://teammately.ai/docs/coverage-engineering/coverage-gaps#task-steps-coverage-gaps","markdownUrl":"https://teammately.ai/docs/coverage-engineering/coverage-gaps.md","sectionId":"task-steps-coverage-gaps","kind":"task","productArea":"coverage_engineering","score":1284.9540986277725,"reasons":["search_match","term_match","prefix_or_fuzzy_match"],"markdown":"### Task steps: Coverage Gaps\n\n1. Name the behavior area that may be missing: dimension, ontology value, product flow, policy exception, source condition, or boundary scenario.\n2. Inspect **Benchmark Datasets → Representation** and the current Benchmark Version. Check whether the area is absent, represented by too few selected Cases, or represented only by easy examples.\n3. Compare the suspected gap against evaluation failures, failure clusters, Expert Contribution notes, and recent product signals.\n4. Rule out look-alike problems: missing outputs, stale cases, weak applicability logic, overly broad policies, ambiguous rubrics, or output mapping errors.\n5. Route the gap: update Coverage Facets, create a Coverage Story, source or synthesize Cases, request an Expert Contribution, or select already reviewed Cases in Benchmark Datasets.\n6. Review candidates in Case Review, create a new Snapshot when membership changes, and preserve the gap rationale in the owning coverage surfaces.\n\n![Case Pool table with selected cases and an action bar for adding cases to a benchmark.](/docs-assets/assets/screenshots/case-pool-selected-action-bar.png)\n\nWhen a gap points to specific candidates, the operator can select cases and prepare them for benchmark membership."}]}