YouTube CTR Title & SEO Description Optimizer
Generate 10 high-CTR title variations and an algorithmic, chapter-structured video description with keyword tags.
Compatibility & Specs
Compatible AI Models
ClaudeChatGPTGemini
Last UpdatedOct 3, 2026
Customizable Variables4 parameters
Main Prompt Template
175 words • 1219 characters
You are a YouTube Metadata and Packaging Specialist who balances high Click-Through Rate (CTR) with high search ranking. I need title variations and a complete description for an upcoming video.
Video Core Topic & Story:
[video_core_topic]
Target Search Keywords:
[target_keywords]
Key Milestones / Timestamps in Video:
[milestones_timestamps]
Primary Call-to-Action (Link or Next Video):
[primary_cta]
Generate the complete YouTube packaging kit:
1. 10 High-CTR Title Variations: Divided into 3 psychological categories:
- The Curiosity Gap (creating an itch that requires clicking to scratch)
- The Direct Contrast (before/after or expectation vs. reality)
- The Contrarian / Negative Warning ('Stop doing X', 'Why Y failed')
- Ensure all titles are strictly under 60 characters so they don't get truncated on mobile.
2. SEO-Optimized Video Description: An engaging 2-sentence opening summary loaded with primary keywords for the search preview snippet, followed by clickable Timestamps / Chapters, Related Video links, and relevant search tags.
3. The 3-Word Thumbnail Copy Test: 3 complementary short phrases to test on the thumbnail image that pair with the titles without repeating them verbatim.
Tip:Variables marked in purple brackets can be tailored below.
Customize Prompt
Fill in the variables below. Your customized prompt updates instantly in the browser — no AI API needed.
Parameters (4)* Indicates required field
[video_core_topic]
What the video covers and the main takeaway
[target_keywords]
Terms viewers search for on YouTube and Google
[milestones_timestamps]
Key sections of your video for timestamp generation
[primary_cta]
Where you want viewers to go or what to click next
4 parameters active
Live Customized OutputLive Preview
260 wordsYou are a YouTube Metadata and Packaging Specialist who balances high Click-Through Rate (CTR) with high search ranking. I need title variations and a complete description for an upcoming video.
Video Core Topic & Story:
A realistic tutorial showing how to build an AI agent using TypeScript and LangChain that actually works in production, highlighting why naive tutorial code crashes when rate limits hit.
Target Search Keywords:
build ai agent typescript, langchain production tutorial, ai agents for developers, autonomous agents nodejs
Key Milestones / Timestamps in Video:
0:00 Why most AI agent tutorials fail, 1:30 Architecture overview, 3:15 Setting up LangChain & TypeScript, 6:45 Building memory & tool calling, 9:20 Handling rate limits & retries, 12:10 Production deployment.
Primary Call-to-Action (Link or Next Video):
Free GitHub repository with production starter code and link to my video on Vector Databases.
Generate the complete YouTube packaging kit:
1. 10 High-CTR Title Variations: Divided into 3 psychological categories:
- The Curiosity Gap (creating an itch that requires clicking to scratch)
- The Direct Contrast (before/after or expectation vs. reality)
- The Contrarian / Negative Warning ('Stop doing X', 'Why Y failed')
- Ensure all titles are strictly under 60 characters so they don't get truncated on mobile.
2. SEO-Optimized Video Description: An engaging 2-sentence opening summary loaded with primary keywords for the search preview snippet, followed by clickable Timestamps / Chapters, Related Video links, and relevant search tags.
3. The 3-Word Thumbnail Copy Test: 3 complementary short phrases to test on the thumbnail image that pair with the titles without repeating them verbatim.
How to Use This Prompt
Follow this 3-step workflow to extract high-signal responses from any compatible AI model.
01
1. Tailor the Parameters
Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.
02
2. Send to AI Model
Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.
03
3. Review and Iterate
Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.
Prompt Variables & Parameters
Reference breakdown of every dynamic variable embedded in this prompt template.
| Placeholder | Parameter Name | Type | Status | Description & Guidance |
|---|---|---|---|---|
| [video_core_topic] | Video Topic & Story | textarea | Required | What the video covers and the main takeawayDefault: A realistic tutorial showing how to build an AI agent using TypeScript and LangChain that actually works in production, highlighting why naive tutorial code crashes when rate limits hit. |
| [target_keywords] | Target Search Keywords | text | Required | Terms viewers search for on YouTube and GoogleDefault: build ai agent typescript, langchain production tutorial, ai agents for developers, autonomous agents nodejs |
| [milestones_timestamps] | Milestones / Chapters | textarea | Required | Key sections of your video for timestamp generationDefault: 0:00 Why most AI agent tutorials fail, 1:30 Architecture overview, 3:15 Setting up LangChain & TypeScript, 6:45 Building memory & tool calling, 9:20 Handling rate limits & retries, 12:10 Production deployment. |
| [primary_cta] | Primary CTA / Next Step | text | Required | Where you want viewers to go or what to click nextDefault: Free GitHub repository with production starter code and link to my video on Vector Databases. |
Example Execution & Benchmark Output
Sample input arguments and the verified AI response demonstrating expected quality and formatting.
Sample VariablesInputs
[video_core_topic]
A realistic tutorial showing how to build an AI agent using TypeScript and LangChain that actually works in production, highlighting why naive tutorial code crashes when rate limits hit.
[target_keywords]
build ai agent typescript, langchain production tutorial, ai agents for developers, autonomous agents nodejs
[milestones_timestamps]
0:00 Why most AI agent tutorials fail, 1:30 Architecture overview, 3:15 Setting up LangChain & TypeScript, 6:45 Building memory & tool calling, 9:20 Handling rate limits & retries, 12:10 Production deployment.
[primary_cta]
Free GitHub repository with production starter code and link to my video on Vector Databases.
Expected AI ResponseVerified Result
### 1. Ten High-CTR Titles (<60 characters)
**Category A: The Curiosity Gap**
1. How AI Agents Actually Work in Production (41 chars)
2. The AI Agent Mistake Everyone Makes (35 chars)
3. Build an AI Agent That Doesn't Crash (38 chars)
**Category B: Direct Contrast & Specificity**
4. From Scratch to Production AI Agent (TypeScript) (49 chars)
5. I Built a Production AI Agent in 15 Minutes (44 chars)
6. AI Agents: Demo vs Production Reality (38 chars)
**Category C: Contrarian & Warning**
7. Stop Building Fragile AI Agents (33 chars)
8. Why Your AI Agent Fails in Production (38 chars)
9. The Dark Truth About AI Agent Tutorials (40 chars)
10. Don't Build AI Agents Until You Watch This (43 chars)
### 2. Thumbnail Copy Options (Max 3 words)
- Option 1: 'NOT A TOY'
- Option 2: 'WHY IT FAILS'
- Option 3: 'REAL WORLD CODE'
### 3. SEO-Optimized Description Snippet
Learn how to build a production-ready AI agent using TypeScript and LangChain that handles real-world tool calling, rate limits, and memory without crashing.
Grab the complete production code template on GitHub: [Link]
⏱️ TIMESTAMPS:
00:00 - Why Most AI Agent Tutorials Fail
01:30 - Production Architecture Overview
03:15 - Setting Up LangChain & TypeScript
06:45 - Building Memory & Reliable Tool Calling
09:20 - Handling Rate Limits & Network Retries
12:10 - Production Deployment & Monitoring
Best Use Cases
Scenarios and roles where this prompt produces maximum leverage.
Creators finalizing packaging metadata before clicking publish
YouTube channel managers A/B testing title options to revive underperforming videos
Developer advocates optimizing educational tech tutorials for long-term search traffic
Tips for Best Results
Techniques to elevate response fidelity
- •Provide rich background context rather than one-sentence inputs to receive deep, non-generic responses.
- •Engage in multi-turn conversation: use the initial output as a baseline, then ask the AI to sharpen specific sections.
- •Prompt the model to highlight any hidden assumptions or missing trade-offs in its recommendations.
Common Mistakes to Avoid
Frequent failure modes and anti-patterns
- •Giving minimal context and expecting nuanced, expert-level strategic output.
- •Not validating factual references, citations, or statistical claims with verified primary sources.
- •Skipping the customization step and pasting raw bracketed template variables into the AI chat.
Part of Curated Collections
This prompt is sequenced as part of these goal-oriented workflows
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