Imagine this: you spend years building a thriving community of 100,000 followers on Instagram, while the same content on YouTube barely gets a few hundred followers and practically zero views. Sound familiar? I was in that exact situation.
For years, I created videos explaining English idioms using 10-30 second clips from popular sitcoms. On Instagram, people loved the format, and the channel grew. On YouTube, however—although those who found my videos liked them—I failed completely due to a total lack of discoverability. I didn’t care about SEO, so I remained invisible to the YouTube algorithm.
Then, in 2021, the final blow came: due to an error, I locked myself out of my 100k Instagram account, and support was no help. I gave up on the entire multi-year project and didn’t touch it for years.
But a few months ago, I discovered the Suno AI music creation platform. And suddenly, everything changed. A spark ignited: what if I combined my two passions, 80s synth-pop music and teaching English idioms? What if this time, I built not just the content but the entire workflow—from songwriting to multilingual SEO—from scratch, but consciously, with a team of AI partners?
Managing a music education YouTube channel involves a lot of detailed work, including brainstorming, writing lyrics, composing music, creating videos, and writing SEO-optimised metadata in multiple languages. The process can easily take days for a single video. I decided to explore the possibility of consolidating this entire process into a single, cohesive, AI-driven system. The goal wasn’t to outsource creativity but to automate repetitive tasks and involve AI as a partner in the creative process.
This article is a case study of that rebirth: building a 7-step, fully automated music content creation system.
The 7-Step, AI-Driven Music Content Creation System
The system is based on a series of specialised AI assistants (GEMs) that I trained on my own detailed rule sets (Songwriting Guidelines, Intro Rules, etc.) to work according to my channel’s unique style and quality standards.
Step 1: Concept and Brainstorming (From Months to Minutes)
A few years ago, I wrote two books covering 100 common English idioms each. The research was brutal: finding the idioms, writing the explanations, and most importantly, meticulously checking their frequency. This process involved examining the historical popularity of phrases on the Google Books Ngram Viewer and analysing their modern, real-world context in the billion-word COCA (Corpus of Contemporary American English) database. This linguistic deep dive, even with the help of my own Python script, took weeks.
In contrast, my new system uses a “Linguist” AI assistant. Here, reliability comes not from manual research but from the linguistic intelligence condensed into the model. During its training, the AI transformed the statistical relationships between words and phrases into a complex mathematical model. When it decides on frequency, it doesn’t search a database but assesses how probable and natural a phrase is in modern language use based on its model.
The assistant has one job: to identify expressions that meet a strict, four-point criteria based on its internal mathematical model:
- Relevance: Only phrases actually used in modern, everyday English are considered.
- Musical Potential: The selected idioms must be “song-worthy,” capable of capturing a story or emotion.
- Conciseness: Phrases that are too long or difficult to set to a rhythm are excluded.
- Uniqueness: It is guaranteed not to repeat material already covered on the channel.
Result: What used to be months of data-driven research now literally takes a single coffee break.
Step 2: The Virtual “A&R” and Songwriter-Producer
Once I have a list of ideas for an album, the next phase is songwriting. This is where the most advanced member of the system comes in, an AI with the persona of an “A&R Executive and Songwriter-Producer,” which creates a complete, coherent album from a list of 10 idioms.
The Producer’s Mindset: The Album’s Emotional Arc
Instead of treating the songs as separate islands, the AI first analyses the entire list of 10.
Finding a Central Theme: It assesses whether there is a dominant emotional thread among the expressions (e.g., struggle, relationships, optimism). It defines a Central Mood and an Emotional Arc for the album. For example: “The album starts dark and introspective, the middle features more struggling but energetic songs, and it moves towards a cathartic, uplifting conclusion.“
This initial, strategic step ensures that the songs don’t just follow each other randomly. We avoid drastic jumps like “Test Department followed by Rick Astley” and create a genuine, listenable album experience.
The Songwriting Work: Rules and Creativity
Based on the overall concept, the AI writes the lyrics song by song according to my Songwriting Guidelines document, following sophisticated rules:
- Logical Structure Choice: It selects the most appropriate song structure based on the nature of the idiom (e.g., narrative, encouraging, epic).
- Structural Variety: To avoid monotony, it consciously varies the song structures. Some start with the chorus, others feature instrumental sections.
- Creative Explanations: In the didactic Bridge section of the songs, it avoids the “spoken-word” cliché and uses varied solutions: a filtered vocoder voice, a whispering vocal harmony, or an acapella vocal.
- Conciseness: The lines are short, the lyrics are tight, avoiding unnecessary word repetition and rambling.
Sound Engineering Precision: The Suno Prompt
After writing the lyrics, the AI acts as a “virtual sound engineer” to create a highly detailed musical recipe for the song, the Suno AI prompt. This defines not only the style (80s Synthpop) and mood (melancholic, epic) but also includes specifics:
Vocals: male baritone, restrained but emotional, occasional vocoder layers
Drums: LinnDrum-style kick/snare, mechanical groove, subtle reverb
Synthesizers: Juno-60 pads, Prophet-5 atmospheres, filtered pad rises
The Process in Practice: The “burn bridges” idiom
AI Analysis and Structure Choice:
The AI chooses the “classic narrative pop structure” because the idiom is an action-related expression with a strong visual image that signifies the end of a story. The narrative structure is best suited to build up a story—a soured situation or relationship in the verses—that leads to the dramatic and final break in the chorus. The Ultravox-style cinematic, melancholic mood is a perfect fit for portraying such a final decision.
AI Lyrics (excerpt):
[Chorus - The music swells with a powerful bassline and layered synths. The vocals become strong and emotional.]
Tonight I'm going to burn the bridges down
And leave the ashes in this town
No turning back, no second chance
This is the end of our last dance.
AI Suno Prompt:
Genre: 80s Cinematic Synth-Pop, New Wave
Vibe: Melancholic, epic, determined, atmospheric, storytelling
Vocals: Clear and emotional male baritone, in the style of Midge Ure from Ultravox, with powerful harmonies in the chorus.
Tempo: Mid-tempo, 110 BPM
…and so on, right down to the mixing instructions.
Result: With a single command, the AI generates the lyrics for an entire conceptually unified album, along with their ready-to-use musical “recipes.”
The lyrics generated by the Songwriter GEM are never the final versions; they are excellent first drafts. I personally validate every lyric and often revise it 5-10 times to ensure the linguistic and artistic quality is perfect. The system can only work effectively because I can check and refine every step based on my own knowledge.
This is why, for example, you cannot reliably use AI for translation without knowing the target language perfectly. Technology is a tool, not an autonomous expert.
Step 3: The Composer (Suno AI) – Cost-Effective Creativity
After the lyrics and musical concept are ready, the music is composed on the Suno AI platform. My goal here is not to explain how Suno works, but to illustrate the strategy I used to turn random attempts into a guided, cost-effective creative process.
The extremely detailed prompts generated by the “Songwriter-Producer” GEM are key. However, the real breakthrough is the two-step musical instruction:
- The Main Prompt: This is the “recipe” that defines the song’s overall sound, style, instruments, and mixing.
- Embedded Stage Directions: In the previous step, the GEM also inserted instructions into the lyrics that Suno can interpret, such as [Huge synth brass stabs, layered vocals, energetic feel] or [Bridge – spoken-word line over filtered pad, echoing snare]. These are invaluable because they control the song’s dramaturgy and the dynamics of the arrangement at key points in the lyrics, with second-by-second precision.
Before I started to use this method, generating the right quality music for a 10-track album often cost 400-500 Suno credits due to the many trials and refinements. Now, thanks to the detailed, all-encompassing prompts—which define everything from style and instruments to mixing—Suno gets extremely close to the desired final result even in the first few generations.
The result is tangible: Instead of the previous cost of 400-500 credits, I can now produce a full album for a maximum of 150-200 credits. This not only speeds up the workflow dramatically but also makes content production significantly cheaper. The system saves not just time, but money too.
Step 4: The Creative Director (Intro-Generator GEM) – Solving the 90% Drop-off Rate
The world of TikTok and YouTube Shorts is ruthless. While several of my 3-4 minute songs on YouTube achieved around 50% viewer retention, the statistics for my short videos were devastating: I lost over 90% of viewers in the first 2 seconds. The quality of the content became irrelevant because no one stayed to listen.
After a long search and experimentation, I discovered the secret to the “hook”: eye contact, surprise, and a quick, curiosity-arousing question. Static images and fast-changing captions were simply not enough. The solution was a talking character who creates a direct connection with the viewer: my own caricature.
The 49.5% average watch rate shown in the image is already an encouraging sign, even in the first few hours, that the eye-contact-based strategy is working.
Building the Toolkit: Text Templates and Visual Elements
Before I could put the AI in the director’s chair, I had to prepare the toolkit.
- The Psychological Hooks (Text Templates): Using an AI prompt, I developed over 20 different intro templates based on psychological principles (e.g., direct question, creating mystery, highlighting value). I recorded these and the logic for their use (e.g., “use a direct question for a visual idiom, create mystery for an abstract one”) in the GEM’s knowledge base.
- The Visual Elements (Avatars): With a 5-step, AI-assisted graphic design workflow, I created a complete visual set:
- Based on my own portrait, I created the basic style of the caricature using Midjourney.
- I gave the character different emotions (smile, grin, tired).
- I generated various backgrounds in the character’s style.
- By combining the characters and backgrounds, I created nearly 50 variations.
- Using an AI image recogniser, I selected the 10 visually strongest ones.
The GEM as Director
With this precisely built toolkit, the “Creative Director” GEM can now manage an entire studio. Given the concept for a 10-song album, for each song, it:
- Selects the most psychologically effective intro text based on the established logic.
- Holds a “casting call” for the avatar characters and backgrounds to suggest the scene that best fits the song’s mood.
Result: A full album’s worth of unique video intro concepts that, based on early tests, dramatically improve viewer engagement. Of course, we’re not done yet, as these concepts still need to be “brought to life.” Using HeyGen, I can create all the intro videos I need in less than half an hour.
Step 5: The Visual Director (Midjourney Prompt GEM) – Creating a Unified Visual World
An album needs to be consistent, not just in sound, but also visually. I create the images for the video backgrounds and thumbnails using Midjourney, but achieving consistency for 10 songs (which means at least 20 images) is a major challenge.
For this task, I trained a “Visual Director” AI assistant. The process is as follows:
- The “Master Style Image”: I create a single image myself that perfectly captures the desired visual mood of the album.
- Creating the “Visual DNA”: The AI analyzes this Master Image and “extracts” its most important stylistic features (color palette, technique, lighting, mood), then creates a text-based style block from this, the “Visual DNA.”
- Prompt Generation: The AI, considering the finished lyrics and the album’s emotional arc, creates two prompt descriptions for each song to illustrate the story (one for the Verse, one for the Chorus).
- The Final Prompt: Finally, it combines the content descriptions with the Visual DNA and technical parameters (–ar 9:16).
Result: Instead of spending hours experimenting with prompts, the GEM returns 20 perfectly style-consistent but content-unique Midjourney prompts with a single command. Thanks to the precise descriptions, the image generation is almost always successful on the first try.
Step 6: The Automated Editing Room (FFmpeg Scripts) – Replacing Manual Work
Although this step isn’t AI, it’s a crucial part of the overall automation process. The images generated in Midjourney need to be prepared for use: upscaling, resizing for videos (16:9 and 9:16), and creating YouTube thumbnails (with effects, logo, mascot, and text).
This task, which would take hours manually and be extremely monotonous, is handled by an intelligent Bash script using FFmpeg. The script:
- Recognizes the aspect ratio of the images (landscape or portrait).
- Applies different resizing and positioning rules accordingly.
- Adds blur, overlay, logo, a random mascot, and text layers to the images.
Result: The entire post-production for 20 images is completed in less than a minute by running the script. This step is the peak of efficiency improvement in the workflow.
Step 7: The Multilingual SEO Manager – Automating Publication
The last, and perhaps most tedious, part of content creation is publishing. A single video requires a unique title, description, and hashtags—separately for the long video and the Short. Doing this for a 10-song album in 6 different languages (English, Hungarian, German, Spanish, Vietnamese, Indonesian) would be soul-crushing work that takes days.
This is where the final link in the system comes in, the “YouTube SEO & Content Automation” GEM.
The Process
Instead of going song by song, language by language, I give the AI a single list: the 10 idioms and their associated secondary keywords that I researched. From this input, the GEM generates the complete metadata package based on the data stored in its knowledge base (formatting requirements, ranked multilingual keyword lists, templates).
For each song, the GEM:
- Creates Content: Writes a short, 5-word English definition of the idiom and a relevant example sentence.
- Selects Keywords: Chooses the highest-scoring primary keyword for English and all 5 other languages from my own pre-made SEO list.
- Writes Text: Flexibly uses templates to write the titles and descriptions, elegantly weaving the primary and secondary keywords into the beginning of the text for maximum SEO impact.
- Prepares the Package: Produces the complete, ready-to-publish package: the long video’s title, description, and hashtags; the Short’s title and description; and all of this in 6 languages.
Result: What would have been days of tedious administration is now condensed into a single, copy-pasteable response. The complete, ready-to-publish metadata for the entire album is ready in minutes, error-free and optimized for local search habits in every language.
Conclusion: AI as a Creative Team Member, Not a Replacement
This system-building process has proven that AI is not the enemy of creativity, but can be an extremely powerful catalyst for it, provided we give it a clear framework and rules.
The main lessons learned:
- The System is Key: An AI is only as good as the rule system behind it.
- Iteration: The prompts and GEMs had to be continuously refined based on the results. This is not a one-time setup but an ongoing dialogue with the technology.
- The Human Factor: The final creative direction, strategy, and taste remained in my hands all along. The AI functioned as an extremely efficient, tireless, and creative “crew,” but I was the “Director.” AI can never work for us—it can only help.
This workflow allows me to shift the focus from repetitive, time-consuming tasks to creative brainstorming and strategic planning, while the quality and consistency of the content have reached an unprecedented level.
What are your experiences with AI-based creative workflows? I’d love to read your thoughts in the comments!