Run AI prompts
on your YouTube videos.

A different approach to YouTube video prompts.

AI prompts for YouTube videos are instructions you run against a video, or a whole channel, to get structured output back: a summary, chapter markers, a title and description, the themes in the comments, a side-by-side against a competitor’s upload. Creators, content teams, and marketers have used prompt libraries for years to save the hours of manual review that come with a growing back-catalog.

Why rewatching your own back-catalog doesn’t scale

For most creators, the practice never matched the promise. A new upload gets watched once for quality, skimmed for a title, and then left alone. Comments pile up unread past the first hour. A competitor’s channel gets a quick glance instead of a real comparison. The prompts floating around online are generic, built for one video at a time, copy-pasted into a chat window that forgets the last one the moment you close the tab.

Reading the video, not just the transcript

Speak AI treats every YouTube video as more than a wall of text. Each video is transcribed in your language, with 100+ supported, and then the recording itself is analyzed: the tone and energy in the delivery, the pacing of the hook, the visual cues that keep viewers watching past the first ten seconds. Titles, chapters, comment themes, and competitor comparisons come back as structured fields your team can use, not one long block of text to reread.

Then the questions start. Ask across your whole channel with AI chat, running the same prompt library natively over your videos with ChatGPT, Claude, and Gemini built in, so you are never locked to a single model. Query it all again later through Claude, ChatGPT, and Cursor via the MCP server, without leaving the tools you already have open.

AI prompts creators run over their videos

• “Summarize this video in 150 words and pull the three most quotable lines for the description.”
• “Break this video into chapters with timestamps and a one-line description for each.”
• “Draft five SEO-friendly titles and a keyword-rich description for this video, matching the channel’s tone.”
• “Turn this video into a 600-word article with subheadings, plus three short-form clip ideas with in and out timestamps.”
• “Group the comments on this video into themes and tell me what viewers are asking for most.”
• “Compare this video against these three competitor uploads on hook length, pacing, and topics covered.”
• “What emerging topics show up across this month’s uploads that we haven’t covered yet?”

From a video library to a content engine

The result is a channel that reviews itself. New uploads get chaptered and titled before they publish. Comment themes surface what viewers actually want, instead of a scroll through the section by hand. Trends across months of uploads become a report instead of a hunch, and dashboards you can customize and white-label track views, retention, and comment sentiment over time, so this month’s uploads are measured against last month’s. A respected media brand put 500+ hours of conference video through this workflow and turned it into high-performing content, without adding headcount.

And because videos rarely live alone, the same engine scores calls, meetings, and recordings on the same criteria, connecting your YouTube channel to call scoring and coaching across every conversation your team has.