Create an AI Character: A Testable Workflow

A useful AI character workflow separates creative exploration from repeatable quality checks. This guide provides a practical path from the first specification to conversation, image, and video regression tests.

Define the job before the persona

Write one sentence that names the user, the moment, and the desired outcome. A character designed for short fiction needs a different memory and pacing policy from one intended for recurring conversation or visual storytelling.

Build a compact specification

Keep stable identity, relationship boundaries, long-term memory, temporary context, visual identity, and forbidden changes in separate fields. This makes conflicts visible and keeps a recent message from silently replacing the character core.

Run a six-case conversation matrix

  1. First greeting and role clarity.
  2. Paraphrased stable-fact recall.
  3. Delayed recall after twenty turns.
  4. Conflicting user input and clarification.
  5. Boundary response without personality collapse.
  6. Resumed conversation with an unfinished commitment.

Test visual continuity

Generate front, three-quarter, and full-body views across three seeds. Change only one of wardrobe, background, camera, or expression at a time. For video, compare the first, midpoint, and last frames against the approved still-image baseline.

Score and diagnose failures

Use role clarity 20%, factual consistency 20%, personality 20%, memory 15%, boundaries 10%, and visual continuity 15%. Keep hard safety failures outside the average. Classify issues as specification gaps, retrieval misses, priority inversion, summarization loss, image drift, or temporal drift.

Release checklist

Related resources

FAQ

Should every detail be fixed?

No. Fix only identity-critical attributes; scene, mood, and wardrobe can remain controlled variables.

When should the suite run?

Run it after model, prompt template, memory, safety, image conditioning, video, or character-spec changes.

Reproducible test manifest

Do not store only the final score. Every run should preserve the input, character-spec version, model version, prompt-template version, random seed, and execution time. Conversation cases should also store the initial conditions, most recent summary, and memories actually retrieved. Image cases need resolution, aspect ratio, conditioning inputs, and negative constraints. Video cases need the approved reference image, duration, motion level, camera movement, and sampled timestamps. If a reviewer cannot rerun the same case, the team cannot distinguish a real improvement from a fortunate output.

Paired review protocol

Place the previous and candidate outputs on randomized sides and hide which one is newer. Ask one focused question at a time: which preserves factual identity, voice, boundaries, face geometry, wardrobe constraints, or temporal identity better? Two reviewers should score independently. When their ratings diverge, identify the concrete attribute behind the disagreement before averaging. Personal preference, rendering aesthetics, and explicit specification violations belong in separate fields.

Decision log

Record pass, conditional pass, or stop. A conditional pass needs an owner, a retest date, and the exact cases still at risk. Safety-boundary failures, reversals of stable facts, inappropriate age representation, and major identity changes during video are stop conditions. Never delete a difficult case merely to raise the pass rate. Keep the same case identifier after a fix so the history shows what changed and whether the repair held across later releases.

Maintenance cadence

Rerun the regression set after changes to the model, memory retrieval, summarizer, prompt template, image conditioning, video pipeline, or character definition. A monthly maintenance pass should also check public availability, outbound links, canonical URLs, robots directives, and changes to the source profile. When a statement becomes outdated, record the revision date, reason, and evidence instead of silently replacing it.

Disclosure: This maintained resource is published by the Ponys.ai team. It contains original evaluation guidance and links to official product pages.

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