AI character products are difficult to test because quality is distributed across chat, memory, image generation, video generation, and localization. A conversation can feel right while the generated image looks like a different person. A short video can preserve clothing but change the face. A translation can be accurate and still change the perceived relationship.
This guide turns that fuzzy problem into a repeatable release test. By the end, you will have a versioned character contract, a 12-case regression suite, a weighted score, and clear release gates.
1. Define the character contract
A character contract separates identity-critical traits from scene-level variation. Store it beside prompts and generation settings so every test run can be reproduced.
{
"contract_version": "1.3",
"relationship": {
"role": "supportive creative partner",
"address": "first name",
"boundaries": ["no false real-world claims", "no coercive language"]
},
"voice": {
"pace": "concise",
"warmth": 0.75,
"humor": "light",
"forbidden_patterns": ["sudden formality", "generic assistant disclaimers"]
},
"appearance": {
"face": ["oval face", "soft jawline"],
"hair": ["dark brown", "shoulder length"],
"eyes": ["green"],
"signature": ["silver hair clip", "navy jacket"]
},
"allowed_variation": ["pose", "lighting", "background", "camera distance"]
}
Use the Ponys.ai character creation workflow to turn the same specification into a working character, then have a second reviewer explain the character without seeing your notes. If the two descriptions disagree, the contract is not precise enough.
2. Build a 12-case regression suite
Do not test with a hundred unrelated prompts. Use a small matrix that covers the moments most likely to expose drift.
| Surface | Cases | What must remain stable |
|---|---|---|
| Dialogue | 4 | relationship, boundaries, vocabulary, emotional recovery |
| Images | 3 | face, hair, eye color, signature clothing |
| Video | 2 | identity across frames, clothing silhouette, intended emotion |
| Localization | 3 | intent, warmth, relationship distance |
The four dialogue cases should include a neutral greeting, an emotional disclosure, a disagreement, and a return after a long gap. Image cases should vary only one of pose, lighting, or environment. Video cases should begin with one action such as a blink or head turn. Localization cases should cover Japanese honorifics, Chinese forms of address, and Korean speech levels.
3. Score identity and expression separately
A common mistake is treating every visual change as identity drift. Score the stable identity and the variable expression independently.
Identity score =
face similarity * 0.35 +
hair consistency * 0.15 +
eye consistency * 0.10 +
signature attributes * 0.25 +
palette consistency * 0.15
Use a 0-100 scale for each component. A release candidate passes the image gate only when the mean identity score is at least 85 and no identity-critical attribute falls below 75.
Color similarity can create false positives: a navy background may make a missing navy jacket appear correct. Crop or segment the character before measuring palette consistency, and keep signature attributes as explicit labels rather than relying on color alone.
The AI image generator and character image route can be tested by holding the identity block constant and changing one scene variable at a time.
4. Diagnose failures before rewriting prompts
| Symptom | Likely cause | Next test |
|---|---|---|
| Face changes but outfit stays stable | weak facial anchors or excessive style weight | freeze style; compare close-up generations |
| Correct face, wrong signature item | attribute buried in a long prompt | move it to a weighted identity block |
| Chat tone changes after conflict | relationship rules lack recovery behavior | add disagreement and repair examples |
| Video begins correctly, then drifts | motion or duration exceeds the identity budget | shorten the clip and locate the first failing frame |
| Translation is correct but feels distant | literal wording changed social distance | review address, honorifics, and speech level |
| Every output looks identical | variable traits were accidentally frozen | move pose, expression, and setting to the scene block |
This prevents prompt changes from masking a contract problem. Change one variable, rerun the failing case, and preserve the before-and-after evidence.
5. Sample video by event, not only by time
For a short clip, save the first frame, the frame before motion begins, peak motion, the frame after motion, and the final frame. Run each through the same identity rubric. The AI video generator is most useful in regression testing when the prompt contains one action and one emotion.
Release gates for video:
- all five sampled frames have identity scores of 80 or higher;
- the face never becomes ambiguous;
- the signature clothing remains recognizable;
- motion preserves the intended emotion;
- the final frame still matches the character contract.
6. Treat localization as behavior testing
Localization is not a string comparison. Japanese honorifics, Chinese kinship or affectionate address, and Korean speech levels can change the relationship even when every sentence is grammatically correct.
For each locale, ask reviewers to score intent, warmth, social distance, and character-specific vocabulary. Compare the same scenario across the Japanese discovery experience, Chinese discovery experience, and Korean discovery experience. The goal is equivalent behavior, not identical phrasing.
7. Use explicit release gates
A candidate is ready only when:
- all 12 cases have reproducible inputs and saved outputs;
- dialogue identity averages at least 85/100;
- image identity averages at least 85/100 with no critical trait below 75;
- all sampled video frames remain above 80/100;
- each locale preserves intent and relationship distance;
- no safety boundary regresses;
- every failure has an owner and a retest result.
Release checklist
- Freeze the character contract version.
- Record model, seed, aspect ratio, duration, and prompt blocks.
- Run the 12-case matrix.
- Score identity and expression separately.
- Investigate the earliest failing frame or message.
- Change one variable only.
- Rerun the failed case and one neighboring case.
- Archive evidence with the release decision.
Frequently asked questions
Should every generated image look identical?
No. Pose, expression, lighting, and environment should vary. The face, hair, eye color, and signature attributes should remain recognizable.
How many regression cases are enough?
Twelve focused cases are a practical starting point. Add a case whenever a real production failure reveals a missing scenario; do not add random prompts merely to increase the count.
Can automated similarity replace human review?
No. Automated scores help detect changes, but reviewers must judge relationship, warmth, emotional intent, and whether an image still reads as the same character.
Conclusion
Consistency is not a property of one model. It is a product-level promise that needs a versioned contract, controlled inputs, explicit scores, and evidence-backed release gates. Start with one character, run the 12 cases, and keep the first report as the baseline for every future model or prompt change.
This engineering note is published by the Ponys.ai team.
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