- 01
Define the workflow learning job
Treat “ai generated image” 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. During review, preserve the delivery checklist, and ask the accessibility reviewer to record input provenance before the bounded test.
- 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. During review, preserve the handoff draft, and ask the identity reviewer to record camera logic before the dated decision.
- Confirm ownership, consent, and allowed reuse For a reversible workflow, preserve the control log, and ask the accessibility reviewer to record disclosure clarity before the fallback decision.
- Preserve an untouched source and version history For a reversible workflow, preserve the input snapshot, and ask the production lead to record camera logic before the fallback decision.
- Name the reviewer and acceptance condition For a reversible workflow, preserve the handoff draft, and ask the identity reviewer to record revision intent before the fallback decision.
- 03
Test observable controls for ai generated 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 a reversible workflow, preserve the brief version, and ask the rights reviewer to record reversal cost before the acceptance 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 this checkpoint, preserve the evidence table, and ask the production lead to record human approval before the dated decision.
- 05
Approve a reversible production handoff
Before advancing “ai generated 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. During review, preserve the continuity note, and ask the rights reviewer to record identity consent before the editorial approval.