Your first AI video will look wrong. Not broken — wrong: the hands drift, the logo mutates, the voice lands half a beat late. Almost everyone learning how to create AI video blames the model. The model is rarely the problem. The brief is.
What follows is the sequence we use on client work, written to survive whichever tool you open. No product menus, no button names: those change monthly, the job does not.
How to create AI video: the six-step sequence
Creating an AI video for business use takes six steps: define the job, write a script and shot list, generate more takes than you need, select and assemble, add voice, captions and music, then review before anything publishes. Generation is the shortest step of the six. The two steps either side of it decide whether the output is usable.
1. Define the job
One sentence naming the outcome, the viewer and the placement. Cannot write it? Do not generate yet.
2. Script and shot list
Turn the script into rows. One shot per row: subject, action, camera, duration, voiceover line.
3. Generate in volume
Several takes of every shot from a structured prompt. Pin whatever must stay constant.
4. Select and assemble
Choose on whether shots cut together, not on which clip looks best alone.
5. Voice, captions, music
Voice first because it sets pace, then captions, then music. Export every ratio needed.
6. Review, then publish
A named person signs off faces, claims and product accuracy. Everything upstream automates.
Notice how little of that is prompting.
Decide the job before you touch a generator
A video's job is three decisions made before any tool opens: the outcome it must produce, the person watching, and the placement it plays in. A fifteen-second vertical clip in a paid feed and a two-minute explainer on a pricing page are different films with different rules. Teams that skip this generate something attractive, then hunt for somewhere to put it.
Placement quietly sets everything else. Feed placements are muted, thumb-scrolled and judged in about a second, so the payoff goes first and the brand second. A page-embedded explainer is watched deliberately, so it can breathe.
Here is the caveat I would want if I were reading this. If you need one hero film a year, the kind that carries a market position for twelve months, do not do any of this. Hire a crew. Generative video earns its place on many pieces refreshed often, not on one perfect thing.
Why the script and shot list matter more than the prompt
A shot list turns a script into a generation plan: one row per shot, carrying the subject, the camera behaviour, the duration and the voiceover line that shot must cover. Prompts written from a shot list produce clips that cut together. Prompts written from imagination produce a dozen beautiful fragments sharing no lighting, no wardrobe and no direction.
Write the voiceover first and time it out loud. Whatever you can say in the available seconds is the true length of your script, and it is always shorter than the draft. Cut the script into shots against that timing, and only then think about how each shot gets made. The columns that earn their keep are boring: shot number, what is on screen, what moves, how the camera behaves, seconds, and the VO line. Add one more for anything that must stay identical across shots, because that column becomes your consistency checklist in the next step.
The shot list is the artefact clients undervalue most
On pipeline builds, the thing that improves output fastest is not the model choice or the prompt template — it is a shot list someone actually filled in. Teams arrive asking which generator to license. The ones who improve within a week started writing rows first.
Generating: structure, volume and consistency
Generation is a sampling exercise, not a commission. You write a structured prompt — subject, action, setting, camera, lighting, mood — generate several takes of every shot, and keep the one that cuts. Reusing a seed, a reference image or a fixed character description across shots is what stops a face, a product or a colour changing between cuts.
Structure the prompt the way a director gives a note: what is in frame, what it does, where it is, how the camera moves, how it is lit. Keep that structure identical across a sequence; change only the fields that should change.
Volume is not waste, it is the method. A seed gives the other half of control: with the same prompt and settings, the same seed reproduces a near-identical result, so you change one word and see only that word's effect. Without seeds you are not iterating, you are re-rolling.
Clips also come out short — seconds rather than minutes, as of writing — and identity drifts between separate generations, which is exactly why the shot list exists. You are not making a video; you are making twelve small pieces that must believe they came from the same shoot. For the full picture of how AI video generation works end to end, from orchestration and model routing through to publishing, that is the work Tech Vision Era builds and operates for clients as code they own rather than a product they rent. For a single clip with no pipeline around it, a free self-serve tool at free-video-generator.techvisionera.com is all some jobs need.
Pin one thing per shot and most drift disappears
Most consistency complaints come from projects where nothing was pinned at all. Pick the one element viewers notice changing — usually a face or a product — and lock that before anything else. Pinning the whole frame at once produces clips that are consistent and lifeless.
Why does your first AI video look wrong?
First attempts look wrong for three reasons, almost always in this order: the prompt described a picture instead of a shot, nothing was pinned across generations so identity drifts, and the cut is paced for a cinema rather than a feed. None of the three is a model limitation. All three are fixed in the brief and the edit.
| What you are seeing | The usual cause | What fixes it |
|---|---|---|
| Gorgeous clips, none of them cut together | The prompt described a photograph: mood, but no action and no camera | Rewrite as subject, action, camera, duration |
| Face, product or colour changes between shots | Nothing pinned; each generation started from scratch | Reuse a seed, a reference frame, one description |
| It looks fine but people scroll away | Paced like a film: establishing shot, build, point | Delete the first two seconds; put the payoff in front |
There is a fourth cause, harder to admit: the script was weak and the video is doing its job perfectly. A mediocre idea shot on a real camera gets carried by production value — nice light, a real room, someone charming on screen. Generated, it has none of that cover. If three careful passes have not helped, stop adjusting prompts and go back to the script.
So why does the fifth attempt look so much better than the first? Not because the model improved. Because by then you are describing shots instead of pictures.
Assembly: voice, captions, music and aspect ratio
Assembly is where an AI video stops looking like a demo. Voice sets the pace of the cut, captions carry the message for feed viewers watching with sound off, music covers the seams between separately generated clips, and the aspect ratio decides whether your subject survives the crop. Frame every shot for the tightest ratio you will publish, then widen.
- Voice first. Lay the voiceover before timing a cut. Audio is the timeline; pictures fit to it.
- Captions second. Burn them in and read them back against the audio — generated speech mistranscribes brand names and numbers.
- Music third, quietly. Its job is masking the seams between clips. If you notice the track, it is too loud.
- Ratios last. Export 9:16, 1:1 and 16:9 separately, safe area checked in each.
My take: captions are the highest-return five minutes in the process, and the step most often skipped because they feel like admin rather than craft.
What must never publish without a human looking at it
Three things must never publish unreviewed: any frame containing a real person's face or voice, any claim about price, availability or performance, and anything depicting a product that has to match physical reality. A pipeline can render, caption, resize, version and schedule perfectly well on its own. Approval is the one place a human stays permanently in the loop.
The reason is partly regulatory and partly reputational, and the regulatory half is already concrete. YouTube, for instance, requires creators to disclose realistic synthetic content — content making a real person appear to say something they did not, altering footage of a real event, or generating a realistic scene that never happened. The same guidance is clear that not everything needs a label: non-realistic content, minor colour work, caption generation and cloning your own voice for your own dubs are excluded. The line is drawn at realism and meaningful alteration, not at whether a machine was involved. Provenance is moving the same way through the C2PA Content Credentials standard, an open specification for recording where content came from and how it was edited. The reputational half has no rulebook, which is precisely why it needs a person. No policy will tell you that a generated hand in frame three makes your clinic look careless; a colleague watching once will.
Build the gate as a named role, not a line in a document. Pipelines that ask approval of nobody in particular get approval from nobody at all.
So the decision is not which generator to subscribe to. Anyone working out how to create AI video for a real business should write a shot list before the next attempt: open a sheet, put six rows in it, generate against those rows. If that run is usable, you have a process worth automating.