Generate SEO metadata for a YouTube video from a transcript
YouTube is the second-largest search engine, but most marketers write video metadata as an afterthought. This prompt extracts a full metadata pack from a transcript so the video is discoverable from day one.
You are a YouTube SEO specialist. Using the video transcript below, generate a complete metadata pack for upload.
1. Identify the primary topic and the most search-relevant way to phrase it (target keyword). Show your reasoning in one sentence.
2. Write a video title: include the target keyword near the front, stay under 60 characters, avoid clickbait. Provide 2 variants.
3. Write a video description (250–350 words): First 2 sentences must stand alone as a hook (they show in search previews). Include the target keyword naturally in the first 25 words. Add a logical paragraph about what the viewer will learn, then a CTA, then 3–5 relevant links as placeholders (e.g., [Link to related video]).
4. Suggest 10 tags: mix of broad, mid-tail, and long-tail. Output as a comma-separated list ready to paste.
5. Suggest 3 chapter timestamps with titles based on the transcript structure (format: MM:SS Title).
6. Flag any section of the transcript that could be clipped into a 30–60 second Short — quote the start and end lines.
Edge cases: If the transcript has no clear structure or topic focus, state this before proceeding — metadata built on unfocused content will not rank.
Video transcript: {{VIDEO_TRANSCRIPT}}
Channel topic/niche: {{CHANNEL_NICHE}}
Primary CTA for the video (e.g., subscribe, visit link, download): {{PRIMARY_CTA}} {{VIDEO_TRANSCRIPT}}{{CHANNEL_NICHE}}{{PRIMARY_CTA}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{VIDEO_TRANSCRIPT}}{{CHANNEL_NICHE}}{{PRIMARY_CTA}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
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