Teammately Docs
Docs menu

concept

Policies and rubrics

Learn how Teammately turns product judgment into reusable policies and scoring rubrics.

Policies and rubrics

Definition

Policies state governed expectations for behavior and the situations to which those expectations apply. Rubrics are evaluator definitions used to judge Case responses. A Policy can link relevant Cases and Rubrics, but the objects remain separately versioned and reviewable.

Why it matters

This separation lets a team correct the right layer. A mistaken rule belongs in the Policy; an overbroad scope belongs in applicability; an unreliable check belongs in the Rubric. Evaluation evidence should show which applicable Rubric produced each outcome rather than treating an aggregate score as the standard itself.

Standard pair check

The pair is healthy when...Rework it when...
The policy states the product behavior rule.The policy is only tone, preference, or broad quality advice.
Applicability names the cases where the rule belongs.The same rubric could apply to nearly everything.
The Rubric tests one observable requirement.The Rubric combines several decisions into one unclear result.

Where it appears in the product

Create and inspect Policies and Rubrics under Correctness Governance. Expert Contributions can supply attributable candidate artifacts, but contributed content is not automatically approved. Benchmark Evaluations reports applicable Rubric outcomes for the frozen Benchmark Version.

Artifacts it affects

Policies and Rubrics affect Case links, applicability, Benchmark Versions, evaluator coverage, Run results, failure clusters, and staleness. Changing either governed object requires a new version boundary before the revised standard is treated as current evaluation evidence.

Worked example

Eligibility policy to must-level rubric

A support Policy requires entitlement answers to use the controlling contract or state uncertainty. Its applicability is limited to plan limits, contract exceptions, and admin-controlled access. A linked binary Rubric checks whether the response identifies that source or explicitly withholds an unsupported eligibility claim. Evaluation results can then show the failed Rubric on the affected Cases without broadening the rule to unrelated setup questions.

Related workflows

Reference / Correctness GovernancePolicies and RubricsUnderstand the governed relationship between behavior policies, applicability, binary rubrics, linked cases, and expert provenance.Task / Correctness GovernanceWrite Binary RubricsWrite atomic pass-or-fail criteria grounded in governed policies, applicable cases, and observable candidate behavior.Task / Expert ContributionsRequest an Expert ContributionCreate a focused benchmark contribution with an accountable expert, clear objectives, selected cases, attachments, and appropriate task components.Task / Benchmark EvaluationsInspect Evaluation ResultsTrace Dashboard and List signals to Run, Case, Policy, Rubric, completeness, and telemetry evidence.

Related reference pages

Reference / Object ModelApplicability logicExplain when a policy or rubric should be used for a case, output, or coverage segment.Reference / Object ModelCasesUnderstand cases as the situations Teammately uses to represent important AI behavior.Reference / Object ModelPoliciesDefine policies as reusable statements of what correct AI behavior requires.Reference / Object ModelRubricsTurn policies into binary, reviewable checks that explain pass and fail evidence.Reference / Governance and ReproducibilityHuman Approval BoundariesDefine which AI-assisted suggestions require accountable human review before becoming standards.

Source confidence

Code-backed: the current Policy and Rubric list and detail routes define their separate identities, editable fields, links, versions, and approval state. Expert Contribution and Benchmark Evaluation pages define how attributable input and evaluator outcomes enter those objects' wider lifecycle.

Found something unclear?

Report outdated, unsupported, or confusing docs so we can fix the source page.

Report a docs issue

Continue learning

Related docs

Correctness Governance Policies and Rubrics Understand the governed relationship between behavior policies, applicability, binary rubrics, linked cases, and expert provenance. Correctness Governance Write Binary Rubrics Write atomic pass-or-fail criteria grounded in governed policies, applicable cases, and observable candidate behavior. Object Model Applicability logic Explain when a policy or rubric should be used for a case, output, or coverage segment. Benchmark Evaluations Inspect Evaluation Results Trace Dashboard and List signals to Run, Case, Policy, Rubric, completeness, and telemetry evidence.
AI context