- 01
Define the workflow learning job
Treat “ai angles” 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. In the decision log, preserve the control log, and ask the workflow owner to record visible continuity before the evidence refresh.
- 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. In the decision log, preserve the claim inventory, and ask the brand reviewer to record evidence freshness before the evidence refresh.
- Confirm ownership, consent, and allowed reuse Before revision, preserve the claim inventory, and ask the source custodian to record input provenance before the scope confirmation.
- Preserve an untouched source and version history Before revision, preserve the continuity note, and ask the production lead to record revision intent before the scope confirmation.
- Name the reviewer and acceptance condition Before revision, preserve the delivery checklist, and ask the accessibility reviewer to record temporal order before the scope confirmation.
- 03
Test observable controls for ai angles
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. Before revision, preserve the failure note, and ask the creative lead to record claim scope before the scope confirmation.
- 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. During review, preserve the input snapshot, and ask the creative lead to record revision intent before the release review.
- 05
Approve a reversible production handoff
Before advancing “ai angles”, 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. For this decision, preserve the review copy, and ask the brand reviewer to record evidence freshness before the reversible handoff.