- 01
Define the workflow learning job
Treat “image ai generator” as a search job to investigate, not as proof that a SEELE feature exists. First turn the query into a repeatable sequence with explicit inputs, review points, and a reversible handoff. 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 the named reviewer, preserve the evidence table, and ask the channel editor to record disclosure clarity before the production checkpoint.
- 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 the named reviewer, preserve the review copy, and ask the channel editor to record format readiness before the editorial approval.
- Confirm ownership, consent, and allowed reuse For this checkpoint, preserve the delivery checklist, and ask the identity reviewer to record reversal cost before the bounded test.
- Preserve an untouched source and version history For this checkpoint, preserve the reference set, and ask the claims reviewer to record source fidelity before the editorial approval.
- Name the reviewer and acceptance condition For this checkpoint, preserve the claim inventory, and ask the creative lead to record failure conditions before the bounded test.
- 03
Test observable controls for image ai generator
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 checkpoint, preserve the rights memo, and ask the claims reviewer to record revision intent before the bounded test.
- 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 a reversible workflow, preserve the rights memo, and ask the brand reviewer to record identity consent before the scope confirmation.
- 05
Approve a reversible production handoff
Before advancing “image ai generator”, 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 revision, preserve the rights memo, and ask the delivery owner to record reversal cost before the evidence refresh.