- 01
Define the workflow learning job
Treat “artificial intelligence creates images” 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. Before moving on, preserve the source ledger, and ask the accessibility reviewer to record failure conditions before the source comparison.
- 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. Before moving on, preserve the authorization record, and ask the continuity editor to record format readiness before the controlled revision.
- Confirm ownership, consent, and allowed reuse Before approval, preserve the evidence table, and ask the source custodian to record control availability before the reversible handoff.
- Preserve an untouched source and version history Before approval, preserve the rights memo, and ask the continuity editor to record disclosure clarity before the reversible handoff.
- Name the reviewer and acceptance condition Before approval, preserve the authorization record, and ask the brand reviewer to record format readiness before the reversible handoff.
- 03
Test observable controls for artificial intelligence creates images
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. Before approval, preserve the reference set, and ask the continuity editor to record visible continuity before the dated decision.
- 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. Before delivery, preserve the test fixture, and ask the accessibility reviewer to record format readiness before the source comparison.
- 05
Approve a reversible production handoff
Before advancing “artificial intelligence creates images”, 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 this decision, preserve the test fixture, and ask the identity reviewer to record control availability before the controlled revision.