TL;DR: Three copy-paste prompts that handle YouTube titles, hooks, and SEO descriptions. Works on GPT-4, Gemini, or Claude. Any model you’re already using.
What’s in the Pack
Someone on Reddit dropped three prompts they run every week for their YouTube workflow. No preamble, no theory. Just the prompts.
Here’s what you get:
- Title Prompt: Generate 10 viral titles under 60 characters, with a CTR score for each
- Hook Prompt: Write 5 hooks under 80 words, each rated out of 10
- SEO Description Prompt: Full YouTube description with your keyword in the first two lines, timestamps, hashtags, and a subscribe CTA
The character constraints are intentional. YouTube cuts off titles past 60 characters in most browse feeds, so every title in that list is actually usable without editing. The hook limit of 80 words forces the model to stay tight. Rambling intros lose viewers in the first 30 seconds, and the model accounts for this when you frame the constraint correctly in the prompt.
The SEO description prompt is the most complete of the three. It places your keyword in the first two lines because that’s what YouTube’s algorithm indexes first. It also bundles timestamps, hashtags, and a subscribe CTA into one output, so you’re not bouncing between five different prompts just to fill out a single description field. One run, fully packaged.
The whole pack was built around one idea: zero blank fields at upload time.
Why the Scoring Matters
Most people prompt for output. These prompts ask for output plus a ranking.
The title prompt returns CTR scores. The hook prompt rates each hook out of 10. You don’t have to guess which one to pick. The model tells you. That’s the difference between prompting and prompting well.
Here’s why this actually works. When you ask a model to generate 10 titles without scoring, you get 10 options that feel roughly equal. You end up choosing based on gut feel or whatever sounds catchy in the moment. When you ask for CTR scores alongside each title, the model has to reason about what makes each option stronger or weaker before it can assign a number. That reasoning gets baked into the output. A score is just a number, but the gap between a 62 and an 81 is information you can act on.
Same logic applies to hooks. A hook rated 7/10 next to one rated 9/10 tells you something real. You can follow up by asking the model to explain the difference, then use that explanation to improve the lower-scoring options yourself. You’re not just consuming output. You’re building a feedback loop between your judgment and what the model produces, which makes you sharper at this over time. That compounding effect is worth more than any single title the prompt generates.
📋 Use Cases
- Batch recording session coming up? Run the title prompt first so you’re not staring at a blank field at upload time. Do it the night before, pick your top three, and let them sit overnight. You’ll often change your mind by morning, and that’s a good sign your judgment is engaged.
- Repurposing a newsletter issue or blog post into a video? Feed the original content into the prompt as additional context. These prompts work on any existing content, not just raw ideas. The model can pull keywords and angles directly from what you already wrote, which usually produces tighter, more specific titles than starting from scratch.
- Testing hook variations before committing to a Shorts or long-form intro. Film two versions of your opening 30 seconds based on the top two hooks from the prompt. Check audience retention on both after 48 hours. The data will show you which hook style lands with your specific audience, and that’s a signal worth carrying into every video after.
Prompt of the Day
“You are a YouTube SEO expert. Create 10 viral titles for [TOPIC]. Include power words. Under 60 chars. Add click-through rate score for each.”
Run the hook and description prompts right after. Full video prep in under 10 minutes.
One addition worth making before you paste this anywhere: add a line about your audience. Something like “My audience is [X who wants Y].” That context shifts which power words the model reaches for and makes the titles feel like they were written for your channel rather than pulled from a generic template. The difference between “5 AI Tools That Actually Work” and “5 AI Tools That Save Freelancers 10 Hours a Week” is one sentence of audience context in your prompt. More specific in means more useful out, regardless of which model you’re running this on.
The hook and description prompts respond the same way. Generic input produces generic results. Give the model something to work with and the output will show it.
Try It This Week
Save all three in a doc you can actually find. Run them before your next upload and compare what the model gives you against what you’d write solo. The gap is usually embarrassing.
The point isn’t to hand the whole process off to a model. It’s to show yourself where you’ve been leaving performance on the table. Once you see that gap consistently, you stop writing titles from scratch. You use this pack as a starting point, edit the top results to match your voice, and ship faster with stronger output than you’d have produced alone. Run it once this week and you’ll understand why someone made it a non-negotiable part of their weekly workflow.
YouTube Content Creation Prompt Pack — copy paste and use
by u/promptshopp in ChatGPTPromptGenius