- 01
Define the generation workflow job
Treat “image generator ai” as a search job to investigate, not as proof that a SEELE feature exists. First write a shot or asset contract, test one controlled variation, and plan the human finishing work. Write the intended audience, source owner, desired change, protected details, reviewer, and delivery condition before selecting any interface or model. That brief keeps the evaluation specific and makes an unsupported assumption visible early. At handoff, preserve the rights memo, and ask the workflow owner to record visible continuity before the dated decision.
- 02
Prepare inputs for image generation
For this topic, assemble a visual brief, authorized references, composition goals, style constraints, exclusions, and output requirements. Record where each source came from, who may use it, and what must remain unchanged. Use a small representative asset for the first pass, keep the original untouched, and define a fallback route so experimentation cannot silently become the production master. At handoff, preserve the test fixture, and ask the continuity editor to record evidence freshness before the reversible handoff.
- Confirm ownership, consent, and allowed reuse At this stage, preserve the brief version, and ask the brand reviewer to record temporal order before the production checkpoint.
- Preserve an untouched source and version history At this stage, preserve the source ledger, and ask the release approver to record revision intent before the production checkpoint.
- Name the reviewer and acceptance condition At this stage, preserve the test fixture, and ask the workflow owner to record control availability before the production checkpoint.
- 03
Test observable controls for image generator ai
A bounded evaluation should inspect subject fidelity, composition, typography, material detail, variation strategy, and revision consistency. Change one meaningful variable at a time and record the date, workspace, account context, input, setting, result, and failure. Topic selection can prioritize the question, but it does not establish availability, quality, speed, licensing, or a supported SEELE workflow. At this stage, preserve the control log, and ask the release approver to record source fidelity before the release review.
- 04
Review evidence, safety, and policy boundaries
A search query does not prove access to a model, commercial rights, exact dimensions, or consistent output quality. Use only authorized media, separate observed behavior from marketing language, and check current first-party documentation for any product-specific claim. For third-party products, competitors, plans, models, and platform rules, attach a verification date and primary source; an absent statement is an evidence gap rather than proof of a limitation. For the working record, preserve the authorization record, and ask the rights reviewer to record format readiness before the fallback decision.
- 05
Approve a reversible production handoff
Before advancing “image generator ai”, compare candidates with the brief, inspect fine detail and text, and record which instruction caused each useful change. Document remaining manual work, unresolved evidence, destination requirements, and the person accepting the result. The handoff should preserve sources and test notes, allow correction, and avoid promises about output quality, turnaround, business performance, publishing, or access that the evidence does not support. For a controlled test, preserve the authorization record, and ask the source custodian to record source fidelity before the bounded test.