How do you edit video with AI tools?

Most of the time in editing a talking-head video goes to tasks that are not creative at all: listening through an hour of footage to find the good answers, trimming every "um," hunting for a shot of the building you mentioned, and typing captions. AI tools are now good at exactly those jobs, because they rest on two mature abilities: speech recognition, which turns spoken words into time-stamped text, and visual search, which matches a written description to what appears in the frames.

This guide walks through a real workflow for a common job: turning a 40-minute interview with a local bakery owner into a three-minute profile. It uses Adobe Premiere Pro's built-in AI features as the main example and OpenAI's free Whisper model for captions, but the same steps exist in other editors under different names.

Step 1: edit the transcript, not the timeline

Premiere Pro's Text-Based Editing starts by transcribing your footage, either automatically when you import it or clip by clip, with an option to label who is speaking. You then work in the transcript as if it were a document. Highlight a sentence in the source transcript and insert it, and the matching video lands on the timeline. Delete words from the sequence transcript, and Premiere removes those frames and closes the gap, which editors call a ripple edit.

For the bakery interview, the rough cut goes like this:

1. Import the footage with automatic transcription switched on and speaker labels enabled.
2. Read the transcript and highlight the six or seven answers that tell the story: how the bakery started, a typical morning, a favorite customer.
3. Insert them in order. You now have a rough cut of perhaps five minutes.
4. Use the speaker filter to remove all of the interviewer's questions at once.
5. Search for filler words and pauses, review them, and delete them in bulk. Adobe lets you set the minimum pause length it detects, so you can keep natural breaths.

Fix names first. The transcript will misspell unusual words, so correct the owner's name and the street name with find and replace before you build anything on it.

Step 2: find b-roll by describing it

B-roll is the supporting footage that plays over someone's voice: hands shaping dough, the shop sign, a tray coming out of the oven. Finding it in hours of clips is tedious. Premiere's Search panel analyzes your footage with models that run on your own computer, then lets you type a description and returns the shots that match. It also searches spoken words and metadata such as shoot date or camera at the same time.

Adobe's advice for better results is to be descriptive and use several words, including lighting or camera angle. Compare:

Two limits are worth knowing. The model looks at a reduced version of your footage as still images, so it misses small details and fast motion more easily. And visual search currently works in English only. Adobe says the footage, the analysis and your searches stay on your computer and are not used for training.

Step 3: draft captions, then correct them

Premiere can generate captions from the sequence transcript once the edit is locked. If you use another editor, or want a free option, OpenAI's open-source Whisper model does the same job from the command line. With Whisper and the ffmpeg tool installed, one command writes a plain-text transcript and two standard caption files, .srt and .vtt:

whisper final-cut.mp4 --model small --language en --output_dir captions

Import the .srt file into your editor as a caption track. The --initial_prompt option lets you pass a few words of text to guide spelling, which helps with names:

whisper final-cut.mp4 --model small --language en --initial_prompt "Boulangerie Lavoie, Marie-Claude Lavoie, rue Principale" --output_dir captions

Then watch the whole video with captions on and correct every error. Deaf and hard-of-hearing viewers follow the video through its captions, so every error matters.

Which edits are safe to automate?

Think of each AI step as producing a draft. The question is how costly a mistake would be and how easily you would notice it.

The last row matters most. Software can find every sentence about the bakery's history in seconds, but deciding which one is true to the person, and which cut would twist what they meant, is your job. A deleted "not" or a sentence moved next to the wrong question can change a statement completely, so watch the final cut from start to finish, at normal speed, before it goes out.