- 01
Define the tool evaluation job
Treat “image generation ai” 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 the working record, preserve the decision history, and ask the workflow owner to record control availability before the workflow transfer.
- 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 working record, preserve the reference set, and ask the release approver to record evidence freshness before the acceptance review.
- Confirm ownership, consent, and allowed reuse For a controlled test, preserve the handoff draft, and ask the continuity editor to record identity consent before the editorial approval.
- Preserve an untouched source and version history For a controlled test, preserve the decision history, and ask the delivery owner to record claim scope before the editorial approval.
- Name the reviewer and acceptance condition For a controlled test, preserve the control log, and ask the release approver to record revision intent before the editorial approval.
- 03
Test observable controls for image generation ai
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 controlled test, preserve the test fixture, and ask the workflow owner to record reversal cost 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. In the decision log, preserve the reference set, and ask the identity reviewer to record disclosure clarity before the acceptance review.
- 05
Approve a reversible production handoff
Before advancing “image generation ai”, 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 moving on, preserve the reference set, and ask the model evaluator to record evidence freshness before the reversible handoff.