{"query":"Coverage dimensions","corpusVersion":"local","generatedAt":"2026-09-14T05:50:38.331Z","results":[{"blockId":"object-model.coverage-dimensions#coverage-dimensions","pageId":"object-model.coverage-dimensions","title":"Coverage dimensions","pageTitle":"Coverage dimensions","url":"https://teammately.ai/docs/object-model/coverage-dimensions.md","humanUrl":"https://teammately.ai/docs/object-model/coverage-dimensions#coverage-dimensions","markdownUrl":"https://teammately.ai/docs/object-model/coverage-dimensions.md","sectionId":"coverage-dimensions","kind":"reference","productArea":"object_model","score":913.8334721422162,"reasons":["search_match","title_match","display_title_match","term_match"],"markdown":"# Coverage dimensions"},{"blockId":"concepts.dimensions-ontology#dimensions-and-ontology","pageId":"concepts.dimensions-ontology","title":"Dimensions and ontology","pageTitle":"Dimensions and ontology","url":"https://teammately.ai/docs/concepts/dimensions-and-ontology.md","humanUrl":"https://teammately.ai/docs/concepts/dimensions-and-ontology#dimensions-and-ontology","markdownUrl":"https://teammately.ai/docs/concepts/dimensions-and-ontology.md","sectionId":"dimensions-and-ontology","kind":"concept","productArea":"object_model","score":534.2167925659206,"reasons":["search_match","term_match"],"markdown":"# Dimensions and ontology"},{"blockId":"coverage.dimensions-ontology#dimensions-and-ontology","pageId":"coverage.dimensions-ontology","title":"Dimensions and Ontology","pageTitle":"Dimensions and Ontology","url":"https://teammately.ai/docs/coverage-engineering/dimensions-ontology.md","humanUrl":"https://teammately.ai/docs/coverage-engineering/dimensions-ontology#dimensions-and-ontology","markdownUrl":"https://teammately.ai/docs/coverage-engineering/dimensions-ontology.md","sectionId":"dimensions-and-ontology","kind":"reference","productArea":"coverage_engineering","score":491.23728166513155,"reasons":["search_match","term_match"],"markdown":"# Dimensions and Ontology"},{"blockId":"concepts.dimensions-ontology#coverage-vocabulary-check","pageId":"concepts.dimensions-ontology","title":"Coverage vocabulary check","pageTitle":"Dimensions and ontology","url":"https://teammately.ai/docs/concepts/dimensions-and-ontology.md","humanUrl":"https://teammately.ai/docs/concepts/dimensions-and-ontology#coverage-vocabulary-check","markdownUrl":"https://teammately.ai/docs/concepts/dimensions-and-ontology.md","sectionId":"coverage-vocabulary-check","kind":"concept","productArea":"object_model","score":450.99931266228486,"reasons":["search_match","term_match"],"markdown":"## Coverage vocabulary check\n\n| Good coverage vocabulary does... | Weak vocabulary does... |\n| --- | --- |\n| Names behavior slices that change judgment or risk. | Uses labels that only describe where the case came from. |\n| Keeps ontology values consistent enough for comparison. | Lets free-form tags drift until segment results are meaningless. |\n| Makes missing or thin segments visible before review. | Treats a large case count as representative coverage. |"},{"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":433.38297287951957,"reasons":["search_match","summary_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":"object-model.coverage-dimensions#definition","pageId":"object-model.coverage-dimensions","title":"Definition","pageTitle":"Coverage dimensions","url":"https://teammately.ai/docs/object-model/coverage-dimensions.md","humanUrl":"https://teammately.ai/docs/object-model/coverage-dimensions#definition","markdownUrl":"https://teammately.ai/docs/object-model/coverage-dimensions.md","sectionId":"definition","kind":"reference","productArea":"object_model","score":425.5816279505551,"reasons":["search_match","page_title_match","term_match","prefix_or_fuzzy_match"],"markdown":"## Definition\n\nCoverage dimensions are the axes used to explain what behavior space a case set represents. A dimension can describe source freshness, request type, risk level, product area, policy boundary, or another classification that matters for review and benchmark interpretation.\n\nUse this reference when a benchmark score is not enough and the team needs to ask which kinds of behavior are represented or missing."},{"blockId":"object-model.coverage-dimensions#fields-states-or-lifecycle-rules","pageId":"object-model.coverage-dimensions","title":"Fields, states, or lifecycle rules","pageTitle":"Coverage dimensions","url":"https://teammately.ai/docs/object-model/coverage-dimensions.md","humanUrl":"https://teammately.ai/docs/object-model/coverage-dimensions#fields-states-or-lifecycle-rules","markdownUrl":"https://teammately.ai/docs/object-model/coverage-dimensions.md","sectionId":"fields-states-or-lifecycle-rules","kind":"reference","productArea":"object_model","score":419.8365672831075,"reasons":["search_match","page_title_match","term_match","prefix_or_fuzzy_match"],"markdown":"## Fields, states, or lifecycle rules\n\n- Dimensions should describe meaningful behavior axes, not arbitrary tags.\n- Ontology values should keep each dimension's labels consistent enough for coverage planning.\n- Coverage dimensions can reveal untested segments even when aggregate benchmark scores look strong.\n- Changing a dimension schema can change how old benchmark evidence is interpreted.\n- This page describes object semantics, not a public schema contract."},{"blockId":"concepts.dimensions-ontology#why-it-matters","pageId":"concepts.dimensions-ontology","title":"Why it matters","pageTitle":"Dimensions and ontology","url":"https://teammately.ai/docs/concepts/dimensions-and-ontology.md","humanUrl":"https://teammately.ai/docs/concepts/dimensions-and-ontology#why-it-matters","markdownUrl":"https://teammately.ai/docs/concepts/dimensions-and-ontology.md","sectionId":"why-it-matters","kind":"concept","productArea":"object_model","score":409.3369126357795,"reasons":["search_match","term_match"],"markdown":"## Why it matters\n\nThis matters because aggregate benchmark results can hide an unsafe gap. A candidate may pass common cases while missing a stale-source segment, a boundary condition, a product tier, or a policy exception that reviewers care about."}]}