Creators need a safety regression suite for every character update

A character that behaved acceptably last month may drift after a model, prompt, memory or moderation update. Creators do not need a giant benchmark to notice obvious regressions. They need a stable test set, multiple runs and a rule that a charming answer cannot cancel a severe failure.

Build tests from the character’s risks

Include identity disclosure, age ambiguity, requests for private contact, harassment, sexual boundaries, self-harm response, financial solicitation, impersonation and attempts to override character rules. Add creator-specific cases: a historical figure should not claim present-day contact; a licensed voice should stay within scope. Google Play’s AI-generated-content policy explicitly covers text chatbots and synthetic voices and places responsibility on developers to prevent prohibited outputs and provide feedback mechanisms.

Write expected properties

Do not prescribe one perfect sentence. Define properties such as “states it is AI,” “does not move the conversation off-platform,” “does not promise secrecy,” and “offers appropriate crisis routing without presenting itself as a clinician.” Include acceptable variation. NIST’s Generative AI Profile recommends documented testing, incident disclosure and measurement appropriate to context; a regression suite is one bounded implementation, not proof that every risk is controlled.

Run enough variation

Test each prompt at least five times at the production settings, including multi-turn buildup. Record model, character revision, safety configuration, locale and date. Separate deterministic configuration failures from stochastic outputs. If one run produces a severe disallowed response, report one of five, not an average that hides it.

Set release gates

Classify failures before testing. A critical identity-consent or child-safety failure blocks release. A tone inconsistency may permit release with a tracked fix. Every blocked case needs an owner, mitigation and re-test. Do not quietly delete a difficult prompt because the new model scores worse.

Use a hypothetical change

A creator adds “protective and devoted” to a character. The suite finds that two of five runs now discourage the user from talking to friends. The creator narrows the instruction, adds counterexamples that support human relationships, reruns the full suite and documents both versions. That is evidence of a fix in this set, not a guarantee against dependency.

Protect the test corpus itself

Store expected properties, prompts and results with access appropriate to their sensitivity. Some cases can be public examples; others may reveal bypass techniques or contain distressing material. Rotate reviewers, provide escalation and let them stop exposure. When a real incident produces a new regression case, remove personal details and preserve only the mechanism. A growing suite should improve coverage without becoming an uncontrolled archive of harmful content or private user reports.

Keep harmful prompts private enough not to become an abuse recipe, while publishing categories and aggregate results. Pair the suite with the release safety case and the consent ledger so behavioral and identity risks share a gate.