The LLM video workflow: describing a video instead of editing one
Watch someone make a video with an LLM in the loop and the odd part is the ratio. Twelve seconds of typing, then a long look at what came back. Making collapses. Judging expands.
Most conversations about AI and video start with the model. What lands on a team is a change in what a person does all day.
Quick answer
An LLM video workflow is a video process where the person describes what they want in words and reviews a result, instead of building it shot by shot on a timeline. The model handles assembly. The human handles intent, brand judgment and approval, which is where nearly all the remaining effort goes.
What is an LLM video workflow, and what work does it remove?
It removes the mechanical middle. Matching type size to the last three videos, exporting six aspect ratios, hunting for the current logo file, holding the legal line on screen long enough to read. None of that is creative direction. Capsule's figure is that 30% of creative time goes to tedious tasks, and that is the share this workflow takes aim at.
What remains is the description and the read. The request gets written by the person who wants the video, and a version comes back while they are still thinking. Where creative team capacity goes is worth measuring before and after.
How does describing intent change the video brief and the review step?
The brief gets shorter and more specific at once. A fourteen field intake form exists because a stranger has to reconstruct your intent later. When the requester describes the video and sees a version immediately, the brief becomes two sentences and a correction.
Review moves earlier, happens more often, and changes hands. The reviewer stops checking whether the editor followed the brief and starts checking whether the output is right. The requester becomes the author. The creative team owns the system and holds the last word on quality.
| Task | On a timeline | When you describe intent | What still needs a human |
|---|---|---|---|
| Writing the brief | Fourteen field form, then a kickoff call | Two sentences from the person who wants it | Knowing what the video is for |
| Assembly | Editor builds the sequence, hunts for assets | Model fills a locked template | Nothing, once the template is approved |
| Brand check | Reviewer inspects every file | System enforces type, timing, color, logo | Approving the template and the exceptions |
What stays hard when an LLM makes the video?
Brand consistency stays hard, because a general model produces plausible video and plausible sits below a brand system's bar. Quality control stays hard, because volume moves the bottleneck to review. Knowing what good looks like stays hard, because that judgment was never written down anywhere.
"No, it's newer. Right now, we are working with an agency partner. So we're working in two ways, both an agency partner, and then we're also doing some AI stuff."
Sales development leader, regulatory compliance software company
Two tracks at once is the normal state. The agency carries the work that needs judgment, the AI track carries the experiments, and nobody has written the rule that tells a new hire which is which. That rulebook is the missing deliverable, and ours is in how to evaluate AI video tools.
Should you build your own LLM video workflow in house or buy one?
Build it if the expensive part is generation. Buy it if the expensive part is enforcement, which for a brand mature company it is.
"We are very far along on our AI journey. We're fortunate to have really brilliant minds working on a lot of proprietary AI tools that we can use. So I'd be very interested to understand how you guys differ, and why it would make sense to use a tool versus something we're building in house."
Sales development leader, regulatory compliance software company
Here is the honest answer. A general model can generate video, and a strong internal team can wire it into your stack. What it will not do on its own is guarantee that the type, the logo behavior and the motion match your brand on every render. Building that layer means encoding your motion design so nobody filling in fields can override it, then maintaining it as the brand changes. That part does not get cheaper as models improve, which is why we stay model agnostic.
Worked example: a launch video, described twice
A product marketer types: sixty second launch video for the new reporting module, audience is existing admins, lead with the time it saves, end on the upgrade path. What comes back uses the launch template, the approved product screens and the standard end card. She swaps one line because the second benefit is stronger, then asks for a thirty second cut for email. Her creative director never saw either file. He approved the launch template, which is where his judgment was worth the most.
FAQ
Do you still need video editors if you use AI video tools?
Yes, and the work changes. Editors and motion designers build the templates the model fills, and they hold the final call on quality.
Should we build our own AI video tool in house?
Build if you want to own generation and can staff it. Buy if you need brand enforcement, because encoding a motion system is design work measured in years.
How do you review AI generated video at scale?
Review the template rather than every file. Approve the system once with brand and legal, then sample output on a schedule.
What does a video brief look like when an LLM makes the video?
Two or three sentences: audience, the one thing the video has to land, the format, and the template it belongs to. The detail that used to fill a brief lives in the template now.