- 01
Define the generation workflow job
Treat “ai generator image” 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. For a controlled test, preserve the delivery checklist, and ask the workflow owner to record claim scope before the acceptance review.
- 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. For a controlled test, preserve the handoff draft, and ask the accessibility reviewer to record visible continuity before the workflow transfer.
- Confirm ownership, consent, and allowed reuse For this decision, preserve the control log, and ask the channel editor to record format readiness before the controlled revision.
- Preserve an untouched source and version history For this decision, preserve the input snapshot, and ask the claims reviewer to record evidence freshness before the controlled revision.
- Name the reviewer and acceptance condition For this decision, preserve the handoff draft, and ask the delivery owner to record control availability before the controlled revision.
- 03
Test observable controls for ai generator image
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. For this decision, preserve the brief version, and ask the release approver to record visible continuity before the controlled revision.
- 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. During review, preserve the evidence table, and ask the claims reviewer to record visible continuity before the acceptance review.
- 05
Approve a reversible production handoff
Before advancing “ai generator image”, 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. Before approval, preserve the continuity note, and ask the accessibility reviewer to record format readiness before the reversible handoff.