YOUTUBE TITLE GENERATOR
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Problem and Context
Problem and Context
Crafting the perfect YouTube video title isn’t just about throwing words together, it’s a data driven science (with a little flair 😆). Titles must:
Align with metadata
Be optimized for SEO
Resonate with audience psychology
Follow high CTR structures
Starting from scratch is tough. Sure, you can use other AI tools, but a tool built for scale, consistency, and that doesn’t complain? 😂
🥁🥁🥁 DRUM ROLL PLEASE...
Introducing: YouTube Title Generator v1!
Introducing: YouTube Title Generator v1!
Generate fresh, algorithm friendly title ideas
Optimize existing titles based on click through and engagement principles
Explain why a title works (or doesn’t) with actionable insights
A/B Test title variations for better performance
Project Objective
Project Objective
To build an AI Agent YouTube Title Generator that helps content creators:
Generate high converting YouTube titles tailored to different content types
Analyze & optimize existing titles using proven YouTube patterns
Provide explanations for title effectiveness (psychology + algorithm insights)
A/B test title variations to maximize click-through rate (CTR) and engagement
Adapt recommendations based on the content niche and target audience
This AI Agent ensures every YouTube title is:
SEO-friendly (ranks better in search)
Algorithm-optimized (boosts YouTube recommendations)
Psychologically engaging (triggers curiosity & clicks)
Formatted effectively (concise, structured, high-impact words)
Key Findings and Insights
Key Findings and Insights
I tested the AI Agent on the first video I created as a faceless YouTube content creator in the anime recap niche.
At this stage, I haven’t found the benchmark for evaluating its effectiveness yet and still need to test it across different titles, niches, and markets over time. However, as an initial reference point, I used the VidIQ title score to assess performance. Further refinements and testing are needed. See details below 👇
This was the generated response of the AI agent
This was the generated response of the AI agent
Prompt: Optimize this title: MUSCLE HEAD With ZERO MAGIC Accidentally Becomes The STRONGEST WIZARD
👆 Above is the VidIQ score of the original title.
👇 And here are the scores of the titles optimize by the AI Agent
As you can see the highest score is a whopping 96! According to VidIQ, this score indicates how well your title is optimized in real-time, helping you see its potential effectiveness. A high score like this means a stronger, more discoverable title designed to drive engagement, boost visibility, and align with search trends.
And that’s just one of the simplest ways to use the AI Agent, I only asked it to optimize the title!
Now, imagine creating titles at scale, built on proven, high performing structures, adapting effortlessly to different niches, trends, keywords, SEO, and most importantly... SPEED!
While your competitor is racking their brain over the perfect title, you've already generated 100 AI powered options, shortlisted the top 10, and optimized each one using title structures from top creators, all consistent, all SEO-optimized, all audience-driven.
Speed.
Consistency.
Precision.
AT SCALE.
Struggling to Get AI to Do What You Want?
Struggling to Get AI to Do What You Want?
Want to build a structured, layered prompt for your AI Agent? Applying machine learning frameworks like few shot learning, or a cross-validation instructional prompt that pull knowledge from SharePoint sources in Microsoft 365?
Or maybe ChatGPT isn't generating the output you need, and the deadline is looming while you're racking your brain on what to do…
Been There. Done That. And it SUCKS.
But don’t worry, I got you! 🙌
Let’s Solve It Together!
Let’s Solve It Together!
If you're stuck, reach out—tell me your problem, and I'll help you devise a solution. (FREE!!!)
As someone who lives and breathes AI tools, I feel you. I went from struggling with tech (to the point where I started calling ChatGPT, Copilot, and Claude "brother" 😂 or nearly smashing my keyboard 😅) to learning, applying, experimenting, and continuously iterating different prompts.
I’ve deep dived into OpenAI, Microsoft, Google, AWS documentation on prompt engineering, watched countless videos, and built a repeatable, structured system for getting AI to give you exactly what you need (or even better!).
Data and Tools
Data and Tools
To enhance accuracy and adaptability, I designed three structured knowledge sources:
1st Knowledge Source: YouTube Title References
A dataset of 770 top-performing YouTube titles spanning multiple creators and diverse niches. This dataset includes:
Title Category (Gaming, Finance, Tech Reviews, etc.)
View Count (Performance tracking)
Click-Through Rate (CTR) (Engagement benchmark)
Keywords Used (SEO optimization)
Patterns & Frameworks (Success formulas)
Success Metrics (Retention, search ranking, watch time impact)
2nd Knowledge Source: YouTube Title Strategy Documents
A 54-page document library covering:
SEO best practices for YouTube
Psychological triggers behind high performing titles
Category specific title patterns
Data driven analysis on title effectiveness
3rd Knowledge Source: External Knowledge Links
A SharePoint list ("Reference URLs") containing:
Curated industry sources for up to date insights
Authority rated URLs categorized by relevance
Last updated timestamps to track content freshness
Limitations Faced
Due to platform restrictions (hitting a premium paywall), full dataset integration into the AI agent was not possible.
Tools Used
AI Tools: ChatGPT, Claude
Microsoft 365: Copilot Studio
Techniques: Prompt Engineering
Curious About What I Did After Hitting the Premium Paywall? Click to Find Out!
Curious About What I Did After Hitting the Premium Paywall? Click to Find Out!
Solution
Initially, the AI Agent was designed to leverage three structured knowledge sources—YouTube Title References, YouTube Title Strategy Documents, and External Knowledge Links—to optimize title generation through pattern recognition, data-driven insights, and explainable AI responses.
However, premium access restrictions prevented full dataset integration, meaning the AI couldn’t process or retrieve these resources in real time. This posed a major challenge:
How do you ensure high-quality, algorithm-optimized YouTube titles without direct data ingestion?
The Approach: A Structured & Layered Instruction Framework
The Approach: A Structured & Layered Instruction Framework
Rather than relying on direct dataset integration, I engineered a structured AI prompting framework that emulates knowledge retrieval, decision-making, and iterative refinement.
This transformed the AI Agent into a reasoning-based system that optimizes titles without external data, achieving expert-level insights through structured logic alone (well, it sure does create better titles than me, and doesn’t complain 🤣).
What I Built: A Multi-Layered AI Agent Instruction System
What I Built: A Multi-Layered AI Agent Instruction System
Prompt Engineering – Applied Chain-of-Thought prompting, Self-Consistency, and Few-Shot Learning to guide the AI in producing structured, context-aware title recommendations. While these techniques don’t guarantee perfect reasoning, they improve step-by-step processing, consistency, and response quality.
Decision-Making Structures – Designed a structured AI process using OODA (Observe, Orient, Decide, Act), Expert Systems, and Rule-Based Logic to help the AI follow decision paths and structured evaluation criteria when optimizing titles. While this doesn’t make AI behave like a human, it ensures systematic, logical adjustments to title structure.
Feature Engineering & Human-in-the-Loop AI (HITL) – The AI analyzes key title components (keywords, emotional triggers, curiosity gaps) based on pattern recognition and best practices, but it requests additional input when the context is insufficient, ensuring human oversight refines the final output.
Business Logic & Content Strategy Integration – Integrated frameworks like Hook-Retention-Conversion and AIDA to improve engagement, audience targeting, and title effectiveness. While these frameworks don’t inherently make titles SEO-friendly, they contribute to higher engagement and visibility, which can positively impact search rankings over time.
The Result: An AI Agent That Works! (And Still Has Room for Improvement!)
The Result: An AI Agent That Works! (And Still Has Room for Improvement!)
The AI Agent now performs its intended functionality without direct dataset integration. It analyzes, optimizes, and explains YouTube titles using structured reasoning, ensuring high-performing patterns are followed, even in the absence of external data.
Impact & Next Steps
Impact & Next Steps
What I Learned:
Advanced Prompt Engineering for AI Agents
Microsoft 365 Ecosystem: Power Apps, Power BI, Power Automate, SharePoint
AI Agent Development in Copilot Studio
Applying Machine Learning Concepts to AI Prompting & Instruction Design
Impact:
Optimized Title Effectiveness – generated titles are fine tuned for CTR, engagement, and SEO.
Time Savings for Creators – Automated title generation eliminates guesswork, allowing creators to focus on content.
Improved A/B Testing – Multiple title variations enable easy performance comparison and data driven adjustments.
Education + Optimization – Unlike other AI models, this AI Agent explains why a title works, enhancing creator skills over time.
Next Steps:
Initially, this AI Agent was designed to integrate seamlessly with the three knowledge sources. However, due to premium restrictions, full implementation was not possible.
The intended workflow included:
Pattern Recognition: Leveraging a diverse dataset of top performing YouTube titles to detect and apply successful formulas.
Enhanced Explanations: Using strategic insights from the Title Strategy Documents to provide deeper context and recommendations.
Real Time Data Integration: A planned data scraping pipeline for continuous AI Agent refinement through live YouTube title performance data.
Power Automate Integration: Originally architected for workflow automation.
Additionally, I developed a User Guide/Manual to optimize AI Agent usage, which is currently undergoing further refinement. 😁
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