Teammately Docs
Docs menu

reference

Policies

Define policies as reusable statements of what correct AI behavior requires.

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.

Policies should be read with Applicability logic, Rubrics, Policy versions, and Human Approval Boundaries. Use Create a policy for the operating workflow.

Worked example

Policies boundary

01

Middle

Judgment into standard

Expert judgment
Answers should not claim compatibility unless source data explicitly supports the claim.
Policy
Compatibility claims require explicit source support.
02

Result

Interpretation

Interpretation
The policy explains the standard; linked applicability and rubrics decide when and how a specific output is judged.

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.

Found something unclear?

Report outdated, unsupported, or confusing docs so we can fix the source page.

Report a docs issue

Continue learning

Related docs

AI context