AI Editing
Auto-Captions That Don't Embarrass You
Auto-captions are only useful if they're accurate and match your platform's style. Here's what actually determines caption quality, and how to avoid the ones that misfire.
Why caption quality varies so much
Not all auto-caption systems work from the same input. Some transcribe audio in isolation, with no visual or contextual signal — which is where you get captions that mishear a product name, a person's name, or industry jargon, because the system has nothing but the raw audio to guess from. Systems that have access to the full video context — what's on screen, who's speaking, the surrounding footage — make fewer of those mistakes.
What actually makes captions embarrassing
The failures that get noticed aren't small typos — they're the ones that change meaning or look careless:
- Misheard names, brands or technical terms
- Timing that lags behind or races ahead of speech
- Styling that clashes with the platform — a caption block that doesn't fit vertical video
- No distinction between speakers in multi-person footage
How this fits into a full edit, not a separate step
Treating captions as an isolated add-on step — export video, then run it through a separate captioning tool — is where a lot of the mismatch happens, because that tool has no context on the footage beyond the audio. Broll generates captions as part of the same pass that builds the edit, with access to the full footage and story context, not just an isolated audio file.