From AI Generation to YouTube | Our Protoface, CapCut and Publishing Workflow

It is easy to assume that one good AI video completes the work. In this production, generation was only one layer. The content also needed a clear plan, selected clips, Protoface interface footage, editing, narration, captions, a call to action, a cover and publishing preparation.

The complete sequence was: concept → AI generation → clip selection → screen capture → edit → narration and captions → CTA → thumbnail → YouTube upload. Each stage solved a different audience question.

We reviewed MiniMax H3 and other model options in Protoface while preparing visual material for the promotion. The purpose was not to make a model leaderboard, but to collect clips that could demonstrate different creative possibilities within the service.

Showing generated footage alone did not explain the product clearly enough. Combining the actual Protoface Studio interface with selected outcomes gave the viewer both context and evidence: where the choice happened and what kind of visual material entered the edit.

A long recording of the website made the important controls difficult to notice. We divided the interface into short cuts and enlarged the part that mattered for each beat.

The sequence moved from the Protoface home view to model selection, generation settings, output, key features and the signup benefit. This made the service understandable without asking viewers to study a continuous screen recording.

CapCut became the assembly stage for generated clips, Protoface screen recordings, narration, captions and the final CTA. We trimmed each source to the moment that supported the explanation instead of preserving a clip simply because it had taken time or credits to generate.

The strongest generated moments often needed only two to five seconds. Alternating the interface with the output maintained context, while enlarging one control at a time prevented the mobile frame from becoming a tiny software demo.

A simple claim that a service is good would not match the FourFeetz editorial approach. We preferred a visible structure: use the service, show the resulting material and explain how it entered the workflow.

That approach also made the limits clearer. The promotional video could show our production path without pretending that every model had been benchmarked or that one edited result guaranteed the same outcome for every user.

At upload time, we reviewed the available YouTube settings related to AI-generated or modified content, checked that the affiliate relationship was disclosed, placed the affiliate link and coupon terms in the description, assigned the video to the relevant AI Tools playlist and used a 9:16 cover suitable for Shorts.

These are the items we checked for this publication, not a legal interpretation or a permanent statement of platform policy. Publishing controls and disclosure requirements can change, so creators should review the current YouTube and provider screens when they upload.

The finished process was a chain of editorial decisions rather than a single generation command. Each stage passed only the material needed by the next stage.