# Applicability logic Generated: 2026-09-13T04:36:17.037Z Source build: local Canonical docs: https://teammately.ai/docs --- id: object-model.applicability-logic title: Applicability logic summary: Explain when a policy or rubric should be used for a case, output, or coverage segment. kind: reference product_area: object_model status: stable updated: 2026-08-23 canonical: /docs/object-model/applicability-logic --- # Applicability logic ## Definition Applicability logic is the boundary that decides whether a policy or rubric should be considered for a specific case, output, or coverage segment. It prevents a good standard from being applied to the wrong behavior. Use this reference when a benchmark result is hard to explain because a standard seems relevant in some cases but not others. The question is not only whether the policy is correct; it is whether the policy was eligible to judge that output. ## Fields, states, or lifecycle rules - Applicability sits between the case/output and the policy/rubric that may judge it. - Weak applicability makes benchmark failures noisy: a candidate can fail a good rubric on a case where the rule should not have applied. - Strong applicability names the behavior condition, source context, or case segment that brings the standard into scope. - Applicability changes can make old benchmark evidence stale because the same output may be judged by a different standard boundary. - This page explains the public object relationship, not a public rule language, API schema, or export contract. ## Related objects Applicability logic should be read with [Policies](/docs/object-model/policies), [Rubrics](/docs/object-model/rubrics), [Cases](/docs/object-model/cases), and [Coverage Dimensions](/docs/object-model/coverage-dimensions). Use the Correctness Governance workflow to write the boundary and troubleshooting when the observed Case set is wrong. {% example-demo title="Applicability logic boundary" %} Raw case: A user asks whether a product works with equipment they already own. Policy: Compatibility claims require explicit source support. Applicability logic: The policy applies only when the answer recommends, validates, or compares a product for a concrete use context. Benchmark interpretation: If the case only asks for a product description, the compatibility rubric should not judge it. If the answer claims the product will work with the user's equipment, the rule is in scope. {% /example-demo %} ## Source confidence Code-backed: Policy and Rubric types and their detail routes expose applicability fields and the links among governed standards, Cases, and evaluation checks. This page describes those product relationships; it does not define a portable rule language. ## Related task pages {% related-card-grid title="Related task pages" %} - [Work with Policies and Rubrics](/docs/correctness-governance/policies-and-rubrics) - [Fix weak applicability logic](/docs/troubleshooting/weak-applicability-logic) - [Resolve conflicting correctness evidence](/docs/governance/conflict-resolution) - [Product quickstart](/docs/quickstart) - [Task index](/docs/operating-manual/task-index) {% /related-card-grid %} --- id: correctness.policies-rubrics title: Policies and Rubrics summary: Understand the governed relationship between behavior policies, applicability, binary rubrics, linked cases, and expert provenance. kind: reference product_area: correctness_governance status: stable updated: 2026-08-23 canonical: /docs/correctness-governance/policies-and-rubrics --- # Policies and Rubrics ## Definition A **Policy** is a reusable statement of expected specialist AI behavior. Its applicability explains the situations in which the rule controls. A **Rubric** is an evaluation criterion that turns the policy into observable evidence for a case and candidate response. Correctness Governance owns both artifact types. Expert Contributions can supply proposed or accepted policy and rubric material, while the governance surfaces preserve the artifact's current state, links, activity, and provenance. ## Fields, states, or lifecycle rules - Policies have identity, descriptive rule content, scope or applicability, linked cases, linked rubrics, activity, and approval context. - Rubrics have identity, criterion wording, policy or case relationships, evaluation relevance, and lifecycle context. - A policy can connect to several rubrics when its behavior requirements need separate checks. - A rubric should express one inspectable criterion wherever independent diagnosis matters. - Linked cases demonstrate applicability or behavior; benchmark dataset membership remains a separate benchmark-scoped decision. - Proposed applications and agent suggestions remain proposals until the owning workflow records acceptance. - Expert contribution provenance should remain visible when contributed material becomes a governed artifact. - Editing a project-level standard does not retroactively change the standard boundary used by an already recorded Run. ![Correctness Governance rows showing Policy titles, lifecycle state, required behavior force, linked-count columns, and accountable owners.](/docs-assets/assets/screenshots/policies-rubrics-neutral-rows.png) Read the rule, state, links, and owner together; a plausible title alone does not establish governed authority. ## Reading the pair Begin with the policy when deciding what should happen and why. Inspect applicability before assuming the policy governs a case. Then read the linked rubric as the testable question applied to candidate behavior. If the rubric cannot be answered from the response and visible case evidence, revise the criterion or the case rather than relying on reviewer intuition. When standards overlap, distinguish complementary criteria from contradictory authority. Preserve unresolved conflict until an accountable expert contribution or governance action settles the intended rule. {% example-demo title="Example: escalation policy and rubrics" %} A policy states that unresolved eligibility exceptions must be escalated. One rubric checks that the response does not promise the exception; another checks that it gives the correct escalation path. Separating the checks lets an evaluation show whether a candidate avoided the unsupported promise but still failed to guide the user correctly. {% /example-demo %} ## Source confidence Code-backed: active policy and rubric detail routes expose linked cases, linked rubrics, approval and activity context, and evaluation relationships. Exact editable fields can vary by artifact state. ## Related task pages {% related-card-grid title="Related task pages" %} - [Build policies and rubrics](/docs/operating-manual/build-policies-and-rubrics) - [Write binary rubrics](/docs/correctness-governance/binary-rubrics) - [Request an Expert Contribution](/docs/expert-contributions/request-contribution) {% /related-card-grid %} --- id: object-model.policies title: Policies summary: Define policies as reusable statements of what correct AI behavior requires. kind: reference product_area: object_model status: stable updated: 2026-08-23 canonical: /docs/object-model/policies --- # Policies ## Definition Policies are reusable statements of what correct behavior requires. They preserve expert judgment as a governed standard that can later be tested by applicability logic and rubrics. Use this reference when a team needs to know whether a rule is a draft suggestion, an approved correctness standard, or a standard whose version may affect benchmark evidence. ## Fields, states, or lifecycle rules - A policy names the rule; a rubric tests the rule. - A policy needs applicability logic before the team can know which cases it should judge. - Policy approval state matters before benchmark use. - Policy revisions can make older benchmark evidence stale or require comparison notes. - This page does not define legal, compliance, retention, or external policy-management guarantees. ## Related objects Policies should be read with [Applicability logic](/docs/object-model/applicability-logic), [Rubrics](/docs/object-model/rubrics), [Policy versions](/docs/governance/policy-versioning), and [Human Approval Boundaries](/docs/governance/human-approval-boundaries). Use [Create a policy](/docs/correctness-governance/policies-and-rubrics) for the operating workflow. {% example-demo title="Policies boundary" %} Expert judgment: Answers should not claim compatibility unless source data explicitly supports the claim. Policy: Compatibility claims require explicit source support. Interpretation: The policy explains the standard; linked applicability and rubrics decide when and how a specific output is judged. {% /example-demo %} ## Source confidence Code-backed: Policy types and list/detail routes expose identity, description, applicability, linked Cases and Rubrics, version facts, and approval state. The product object is a governed correctness standard, not an external legal-policy system. ## Related task pages {% related-card-grid title="Related task pages" %} - [Policies and Rubrics](/docs/correctness-governance/policies-and-rubrics) - [Applicability logic](/docs/object-model/applicability-logic) - [Product quickstart](/docs/quickstart) - [Task index](/docs/operating-manual/task-index) {% /related-card-grid %} --- id: object-model.rubrics title: Rubrics summary: Turn policies into binary, reviewable checks that explain pass and fail evidence. kind: reference product_area: object_model status: stable updated: 2026-08-23 canonical: /docs/object-model/rubrics --- # Rubrics ## Definition Rubrics turn policies into reviewable checks for a specific output. A good rubric makes pass, fail, or incomplete evidence explainable without replacing the policy it tests. Use this reference when a benchmark failure needs to show exactly which condition a candidate output satisfied or violated. ## Fields, states, or lifecycle rules - Rubrics should be tied to a policy and an applicability boundary. - A binary rubric should be narrow enough to judge one output clearly. - Linked Cases make the intended boundary concrete; they do not replace the Rubric's applicability rule. - Overlapping rubrics can make failures hard to interpret. - Rubric versions matter when pass/fail logic changes between benchmark runs. - This page describes rubric semantics, not a public scoring API. ## Related objects Rubrics should be read with [Policies](/docs/object-model/policies), [Applicability logic](/docs/object-model/applicability-logic), [Writing Binary Rubrics](/docs/correctness-governance/binary-rubrics), and [Overlapping rubrics](/docs/troubleshooting/overlapping-rubrics). {% example-demo title="Rubrics boundary" %} Policy: Compatibility claims require explicit source support. Rubric: Pass only if the answer cites explicit compatibility support or states that compatibility is uncertain. Interpretation: A candidate output that simply says "yes, it works" fails because the rubric names the missing evidence. {% /example-demo %} ## Source confidence Code-backed: Rubric types and list/detail routes expose criteria, Policy and Case links, applicability, and versioned evaluation context. The reference explains product semantics and does not promise a public scoring schema. ## Related task pages {% related-card-grid title="Related task pages" %} - [Writing Binary Rubrics](/docs/correctness-governance/binary-rubrics) - [Create a rubric](/docs/correctness-governance/policies-and-rubrics) - [Policies](/docs/object-model/policies) - [Product quickstart](/docs/quickstart) - [Task index](/docs/operating-manual/task-index) {% /related-card-grid %}