- 01
Define the tool evaluation job
Treat “ai image creater” as a search job to investigate, not as proof that a SEELE feature exists. First clarify the requested job, observe current controls on a bounded test, and document workflow fit and evidence gaps. 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 reversible workflow, preserve the continuity note, and ask the channel editor 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. For a reversible workflow, preserve the input snapshot, and ask the channel editor to record destination fit before the dated decision.
- Confirm ownership, consent, and allowed reuse For this checkpoint, preserve the failure note, and ask the release approver to record failure conditions before the controlled revision.
- Preserve an untouched source and version history For this checkpoint, preserve the authorization record, and ask the model evaluator to record human approval before the controlled revision.
- Name the reviewer and acceptance condition For this checkpoint, preserve the rights memo, and ask the claims reviewer to record destination fit before the controlled revision.
- 03
Test observable controls for ai image creater
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 claim inventory, and ask the creative lead to record source fidelity before the delivery pass.
- 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 decision history, and ask the delivery owner to record source fidelity before the editorial approval.
- 05
Approve a reversible production handoff
Before advancing “ai image creater”, 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 decision history, and ask the production lead to record failure conditions before the scope confirmation.