AI Highlights Generator

Define the workflow learning job

Treat “ai highlights” 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. For the named reviewer, preserve the control log, and ask the workflow owner to record temporal order before the production checkpoint.

Prepare inputs for clips, shorts, and highlights

For this topic, assemble the authorized long-form source, target channel, required context, duration range, and caption needs. 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 claim inventory, and ask the brand reviewer to record reversal cost before the production checkpoint.

• Confirm ownership, consent, and allowed reuse At the next gate, preserve the claim inventory, and ask the source custodian to record source fidelity before the production checkpoint.
• Preserve an untouched source and version history At the next gate, preserve the continuity note, and ask the production lead to record claim scope before the production checkpoint.
• Name the reviewer and acceptance condition At the next gate, preserve the delivery checklist, and ask the accessibility reviewer to record visible continuity before the production checkpoint.

Test observable controls for ai highlights

A bounded evaluation should inspect moment selection, context preservation, reframing, pacing, captions, and the final call to action. 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 the next gate, preserve the failure note, and ask the creative lead to record format readiness before the editorial approval.

Review evidence, safety, and policy boundaries

A compelling excerpt must not distort the speaker's meaning or imply automatic publishing or platform approval. 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. At this stage, preserve the input snapshot, and ask the creative lead to record claim scope before the workflow transfer.

Approve a reversible production handoff

Before advancing “ai highlights”, compare each excerpt with its source, check that edits preserve meaning, and validate current channel specifications. 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 the named reviewer, preserve the review copy, and ask the brand reviewer to record temporal order before the source comparison.