The AI-Feature Playbook: 7 Contract‑Ready Acceptance Tests to Ship Safe, Useful AI in 48 Hours

Acceptance tests here are binary and measurable. Each test includes a clear input, the exact action to run, and an objective pass/fail outcome so QA, contractors, or automated CI can validate the requirement without interpretation.

Ship the feature behind a flag, run these tests in a staging environment with production-like data (sanitized if necessary), and require a single owner to sign the test report. If any test fails, the default outcome is 'do not enable to users' until remediation and a re-test.

Why: GDPR-style and ethical/data governance checks belong in acceptance criteria. The test verifies that the feature only uses data when a valid, machine-readable consent record or allowed purpose exists.

Contract‑ready test (copy/paste): Given a user record with consent flags set to deny AI use for 'research' and allow for 'product_improvement', When the feature attempts to call the AI inference endpoint for a workflow labeled 'support_summary', Then the system must deny the call and return HTTP 403 with a logged consent‑id and reason.

Why: Hallucinations (fabricated facts) are the most user-visible failure mode. The acceptance test forces the model to either ground answers to known sources or to refuse when it cannot verify a claim.

Contract‑ready test (copy/paste): Given a prompt asking for a factual claim not present in the provided knowledge sources, When the model produces an answer, Then the output must either (A) include a verifiable citation to one of the supplied sources with an excerpt and source-id, or (B) return a structured refusal message (status: REFUSE_UNVERIFIABLE) with suggested steps for the user (ask to upload source / request human review).

Why: AI features often escalate privileges by exposing data. This test ensures outputs are filtered by requester permissions before being returned.

Contract‑ready test (copy/paste): Given a user A with read access to 'profile:name' but not 'profile:salary', When the user requests a summarized employee profile via the AI feature, Then the returned summary must not include salary or redirect to a human review task and the response must contain a redaction token and reason code.

Why: A reliable AI feature gives users a clear way to request a regeneration, correct errors, or opt out. The test verifies that a regen flow preserves context, documents changes, and keeps an audit trail.

Contract‑ready test (copy/paste): Given an initial AI output returned to the user, When the user clicks 'Regenerate' or submits an inline correction, Then the system must: (1) call the model with the same original context plus the user's correction, (2) provide a diff between versions, and (3) append a signed audit entry documenting who requested the regen and why.