AI captions for short-form video: style, timing and placement
In short
For short-form video, show captions one to three words at a time, timed to the speech. Put them in the middle third of the frame, clear of the buttons and text each platform adds at the bottom, and use large, high-contrast letters. Read every caption before you post, because speech recognition often gets names and numbers wrong.

Many viewers follow a clip through its captions
A large share of short-form video is watched with the sound off, at least for the first few seconds — in a queue, in an office, in bed next to someone asleep. Whatever the exact proportion on any given platform, the design consequence is clear: for a meaningful part of your audience, the captions are the only way to follow the clip.
That changes every decision about them. Captions are how many viewers decide, in the first second, whether to keep watching. Treating them as a last-minute checkbox is a common reason a clip with good content underperforms.
It also means the failure is silent. Nobody reports that your captions were unreadable; they scroll, and you see a retention drop with no obvious cause.
Where they come from
Automatic captions are a rendering of the transcript, and the transcript is produced by speech recognition over the audio. Every word carries a start and end time, which is what makes word-level animation possible at all.
Two consequences follow directly.
First, caption accuracy is transcription accuracy. If the audio is clean, modern speech recognition is reliable enough to publish after a skim. If there is crosstalk, heavy accent variation or background music, errors appear — and they appear as confidently rendered on-screen text, which is worse than no captions, because a wrong word in large type is what the viewer remembers.
Second, timing is free but placement is not. The synchronisation comes from the transcript; where the text sits, how much of it appears at once and how it moves are all choices, and they are the choices that determine whether it works.
One to three words at a time
For short-form, show one to three words at once, synchronised to speech. This is the single most consequential styling decision and the one most often got wrong.
The reason is where the eye goes. A full sentence in a block pulls the gaze down and holds it there while the viewer reads at their own pace, disconnected from the audio. One to three words moves with the speech, so reading and listening stay in step and the eye keeps returning to the face.
Full-sentence captions are not wrong everywhere. On a large screen, for long-form content, they are correct — the viewer is settled, the text is small relative to the frame, and reading ahead is a feature. On a phone, in a feed, they read as a wall and they cover the subject.
The other reason word-level captions work is rhythm. Text that appears in time with speech gives the clip a pulse, and pulse is what stops a talking head from feeling static. That is the actual function of the animation — not decoration.
Placement, and the safe area problem
Put captions in the middle third of the frame, and keep them clear of the bottom.
Every vertical platform overlays its own interface on the video: the caption text, the account name, the buttons down the right-hand side. The exact geometry differs per platform and changes without notice. Text placed near the bottom of the frame will be covered on at least one of them, and you will not see it in your editor.
The middle third is the reliable zone. It is clear of the interface everywhere, it is where the eye already is if the speaker's face is centred, and it survives the platform redesign that will eventually happen.
The boundaries are not published by anyone, so they have to be measured: the safe zone checker shows where each platform's interface lands on a frame you drop into it.
Two related rules. Keep text away from the right-hand edge, where the action buttons live. And check on an actual phone before publishing a new caption style — a viewport that is correct on a laptop preview is routinely wrong at the size and aspect people actually watch.
Make the text easy to read first
Contrast matters most. Light text with a dark outline or a soft shadow stays readable over any footage; unoutlined text disappears the moment the background goes pale.
Size should be large enough to read at a glance without dominating the frame — if you are squinting at it in a phone preview, it is too small, and viewers will not squint.
Weight matters more than typeface. Heavy weights hold up against moving backgrounds; light weights vanish. Beyond that, the choice of font is one of the least important decisions available, despite receiving most of the attention.
Colour highlighting on the active word works well and is easy to overdo. One accent colour, used consistently, reads as a style. Three colours read as a broken video.
And keep the style constant across your clips. A consistent caption treatment becomes recognisable in a feed, which is worth more than any individual styling choice — pick one animated caption style and leave it alone.
Accessibility, honestly
Burnt-in captions are not accessible captions. Text rendered into the pixels cannot be resized, cannot be read by a screen reader, cannot be turned off, and cannot be translated.
Where the platform supports uploading a caption track, upload one as well. It costs nothing — the timed text already exists as a by-product of the transcript — and it serves the viewers for whom burnt-in text at your chosen size does not work. If you already have an SRT or VTT file, the free subtitle converter switches between the two formats in your browser.
This is also the honest framing of the "captions increase watch time" claim. They mainly protect watch time from muted viewers. The accessibility benefit is separate, real, and not conditional on whether it helps your numbers.
Fitting captions into the workflow
Because captions derive from the transcript, they are effectively free once the recording is transcribed — which is the same artefact clip selection reads to find moments worth cutting. Doing both from one transcription is what makes the weekly clipping routine fit in an hour.
The part that stays manual is reading each clip's captions before you post. Keep it. It is the cheapest quality control available, and it catches the errors that make an otherwise strong clip look like nobody watched it before publishing.
If you are choosing a tool, compare AI caption generators on the same 60-second file.
Then leave the style alone. A caption treatment you keep for six months compounds; one you redesign every fortnight never becomes recognisable.
Frequently asked
How do AI captions work?
AI captions start with speech recognition, which turns the audio into a transcript with a start and end time on every word. The captions are that transcript drawn on the video, so their timing comes with it and their accuracy matches the transcript. Where the text sits and how many words show at once are your choices.
How many words should a caption show at a time?
For short-form video, one to three words at a time, timed to the speech. Full-sentence blocks are fine for long videos on a large screen, but on a phone they pull the eye away from the face and read as a wall of text.
Where should captions go on a TikTok, Reels or Shorts video?
In the middle third of the frame, above the interface each platform draws near the bottom. TikTok, Reels and Shorts all overlay controls and text there, so captions placed low get covered on at least one of them. The free safe zone checker shows where each interface lands on your frame.
Do captions increase watch time?
They mainly protect watch time. Many viewers start a short-form clip with the sound off, so a clip without captions loses them in the first seconds, however good the content is.
Are burnt-in captions bad for accessibility?
On their own, yes. Text burnt into the picture cannot be resized, switched off, translated or read by a screen reader. Where the platform lets you upload a caption track, upload one as well as the burnt-in captions.
Written by
Founder, GPT-Video
Alessio builds GPT-Video, an AI video editor that turns long recordings into short vertical clips. He works on the clip-scoring and captioning pipeline day to day, and publishes short-form video with the tool while building it: the workflows in these posts are the ones he runs. Before GPT-Video he built Quantly, a multi-agent AI platform for retail trading. He works between Milan, New York and Hong Kong.
Written about in Fortune Italia.
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