- 01
Define the tool evaluation job
Treat “ai generater” 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 named reviewer, preserve the decision history, and ask the continuity editor to record source fidelity before the source comparison.
- 02
Prepare inputs for visual creation tools
For this topic, assemble a clear creative job, authorized references, required controls, reviewer expectations, budget context, and delivery format. 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 named reviewer, preserve the reference set, and ask the factual editor to record identity consent before the controlled revision.
- Confirm ownership, consent, and allowed reuse At this stage, preserve the handoff draft, and ask the production lead to record evidence freshness before the dated decision.
- Preserve an untouched source and version history At this stage, preserve the decision history, and ask the source custodian to record control availability before the dated decision.
- Name the reviewer and acceptance condition At this stage, preserve the control log, and ask the factual editor to record reversal cost before the dated decision.
- 03
Test observable controls for ai generater
A bounded evaluation should inspect input support, controllability, source fidelity, revision behavior, governance, collaboration, and handoff readiness. 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. At this stage, preserve the test fixture, and ask the continuity editor to record disclosure clarity before the dated decision.
- 04
Review evidence, safety, and policy boundaries
Third-party names, pricing, features, access, and specifications require current dated first-party verification. 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 the working record, preserve the reference set, and ask the channel editor to record claim scope before the workflow transfer.
- 05
Approve a reversible production handoff
Before advancing “ai generater”, use a matched test asset, record the date and account context, separate observations from claims, and document tradeoffs. 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 approval, preserve the reference set, and ask the policy reviewer to record input provenance before the final sign-off.