AI-Generated Titles, Descriptions and Tags

Tubeup sends each video's context to Google Gemini and gets back a title, a description, tags, and hashtags — for one video or for an entire batch in a single action. You review what comes back and edit anything you disagree with before uploading. The AI drafts; you decide what ships.

Why generate metadata at all

Metadata is the part of publishing that costs the most time and is the easiest to do badly under pressure. A title, a description, and a tag list are what make a video findable, so skipping them is not an option — but writing them from a blank page for the thirtieth video in a batch is where quality quietly collapses. Generation turns that from writing into editing, which is a much faster and more consistent job.

The trade is honest: you get a structurally sound, keyword-aware first draft in seconds, and you spend your attention on the parts that need judgement. In practice that means titles get a human pass and descriptions usually do not.

What Gemini generates

Field	Shape	What you get
Title	Single line	An SEO-oriented title for the video. Worth reviewing on every video — it drives most of your click-through.
Description	Long form	A full description body. Dynamic descriptions can auto-inject your brand links, social profiles, sponsor URLs, and timestamps.
Tags	List	A keyword tag list drafted from the video's context rather than a fixed template.
Hashtags	List	Hashtags suited to the topic, for use in the title or description area.

Everything in that table is editable after generation. Nothing is locked in until the upload runs.

Dynamic descriptions

Descriptions are the field where automation pays off most, because most of a good description is boilerplate you repeat on every video: your site, your social profiles, sponsor links, a timestamp list. Tubeup's dynamic descriptions inject that material automatically, so the generated text wraps around your standing links rather than replacing them.

Set that boilerplate up once and every video in every future batch inherits it. When a sponsor changes or a social handle moves, you update it in one place instead of editing every description by hand.

Generating for a whole batch

The workflow is built around batches, not single videos. After Tubeup scans your base folder and registers the files, one action generates metadata for everything in the queue. You then read down the list and fix what needs fixing.

If you want tighter control, generate for a single video first, look at the style of what comes back, adjust your settings, and only then run the whole batch. That is a good habit when you are publishing to a new channel with a different tone from your usual one.

Reviewing the output honestly

Treat the generated metadata as a draft with a specific weakness: it is reliably correct in structure and occasionally flat in voice. Read every title. Skim every description for anything that misstates what the video actually contains — that is the failure mode that matters, and it is the one a human catches in two seconds.

Tags and hashtags need the least attention. If a video covers something the AI could not have known from the filename or context you gave it, adding a tag or two by hand is usually all the correction required.

You can also use bulk selection to apply the same correction across many videos at once when the whole batch shares a mistake, rather than editing them one at a time.

Social share copy

Separately from the video metadata, Tubeup can generate post copy for Facebook, X, LinkedIn, and Instagram from the same video context and copy it to your clipboard. It is the same idea applied to promotion: a draft you paste and adjust, instead of writing four platform-specific posts per video by hand.

Next steps

• Run a full bulk upload with generated metadata
• Set up multiple channels with their own defaults