- 01
Define the tool evaluation job
Treat “ai photo creator” 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. At handoff, preserve the decision history, and ask the policy reviewer to record temporal order before the controlled revision.
- 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. At handoff, preserve the reference set, and ask the rights reviewer to record destination fit before the delivery pass.
- Confirm ownership, consent, and allowed reuse While evidence is current, preserve the handoff draft, and ask the model evaluator to record source fidelity before the rights check.
- Preserve an untouched source and version history While evidence is current, preserve the decision history, and ask the rights reviewer to record reversal cost before the evidence refresh.
- Name the reviewer and acceptance condition While evidence is current, preserve the control log, and ask the continuity editor to record visible continuity before the rights check.
- 03
Test observable controls for ai photo creator
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. While evidence is current, preserve the test fixture, and ask the accessibility reviewer to record format readiness before the evidence refresh.
- 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 moving on, preserve the reference set, and ask the factual editor to record camera logic before the editorial approval.
- 05
Approve a reversible production handoff
Before advancing “ai photo creator”, 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. At the next gate, preserve the reference set, and ask the production lead to record identity consent before the production checkpoint.