Creating Hit Music with AI: My Journey from Skeptic to Believer
I used to think AI-generated music was just a gimmick. I pictured robotic beats without emotion and auto-tuned vocals that sounded like they came from a computer lab, not a recording studio. Then I decided to test it myself. What I found changed my entire view on music creation.
Setting My AI Music Challenge
The discussion around AI music seems to divide into two camps: total dismissal or wild hype. I wanted to explore the middle ground and try something specific: could I guide AI to create a complete, radio-ready pop track that actually sounds intentional? I wanted a full Pop-Dance EDM anthem with vocals, a proper song structure, and that polished quality that distinguishes bedroom producers from chart contenders. I mean, the type of track you might actually add to your workout playlist.
Why I Chose Suno.ai
I looked into a few platforms before settling on Suno.ai (Suno.ai, n.d.). The reason I picked it was simple: it generates complete songs with integrated vocals from text prompts. Other tools might provide you with stems or loops to piece together, but Suno.ai crafts the whole package—melody, harmony, rhythm, and vocals all together. My real test was this: could I communicate effectively with this AI to create something that sounds purposeful rather than random?
Learning to Speak AI: The Prompting Process
Creating effective AI music became an exercise in strategic communication. You’re essentially directing an incredibly capable session musician who interprets language in very specific ways.
How My Approach Evolved
I started with what felt natural. My first attempt was simple: “Pop-dance anthem, powerful female pop vocals, energetic, 128 BPM, euphoric supersaw synths, pumping bassline, lyrics about unity and celebration, G minor key.” The result was generic and lifeless.
After several iterations, I learned to be more specific: “Uplifting progressive house EDM pop, anthemic female vocal, infused with Latin Reggaeton dem-bow rhythm and vibrant percussive flair, massive build-up with classic risers, huge synth drop with catchy lead melody, lyrics about finding strength, 128 BPM, high energy. Ensure clear dynamic shifts.” The difference was dramatic. This refined prompt provided the AI with specific sonic reference points, structural expectations, and energy markers, all while following the platform’s guidelines.
Crafting Lyrics That Connect
Instead of just asking the AI to generate random words, I took a strategic approach backed by research. I explored music psychology principles and songwriting theory to understand what makes a song resonate with listeners (The Mystic Keys, n.d.; Catapult, n.d.). My goal was to give the AI a framework to generate lyrics that were not only coherent but also emotionally impactful, even for a beginner like me.
My research led me to focus on several key songwriting elements, which I incorporated into my prompt:
- Lyrical Structure: I learned about the importance of having a clear song structure, with verses, a pre-chorus, and an anthemic chorus (Cambridge Popular Music, n.d.). I used this knowledge to instruct the AI to build a lyrical narrative that builds tension and offers a satisfying release.
- Emotional Hooks: I discovered what makes a song “catchy” and the impact of universal themes and strong imagery. I prompted the AI to focus on relatable feelings like strength and unity, using powerful words to create a vivid emotional picture. This approach is supported by the understanding that repetitive, rhythmic phrasing is a key component of what makes a song an “earworm” (The Mystic Keys, n.d.).
- Rhythmic Phrasing: I found that lyrics with a natural rhythm and repetition are easier to remember and sing along to (Stolpe, n.d.). I suggested the AI use consistent, rhythmic patterns, especially in the chorus, to enhance the song’s memorability and impact.
By providing the AI with a prompt based on these proven songwriting concepts, I created a stronger foundation for the track. The AI then built upon this structure, showing me that my own research and guidance could significantly improve the quality of the output. This proved that even without deep personal expertise, a strategic approach could yield surprisingly good results.
Here’s the lyrical structure that the AI created for my track:
- (Verse 1 – English, strong female vocal) Sunrise breaks on a brand new day, From deepest rhythm, we find our way. Feel the passion rising, strong and pure, A universal anthem, forever endure.
- (Pre-Chorus – English/Spanish, building energy) No more shadows, no more fear, The future’s calling, crystal clear. Hands up high, let the beat guide, This is our moment, nowhere to hide!
- (Chorus – Anthemic, powerful) ¡Unidos en la pista! Feel the fire, deep inside, From every heart, across the tide. Every spirit, wild and free, we ignite the night, Dancing in the starlight, bathed in pure light!
- (Drop – Instrumental focus)
- (Bridge – Reflective, then building) From ancient lands, a wisdom deep, Where timeless stories, our souls keep. Across the mountains, through the endless skies, A new connection, seen through hopeful eyes.
My “Minimal Editing” Experiment
I wanted to see how much AI could do on its own, so I avoided heavy post-processing. Instead of fixing everything the AI might have gotten “wrong,” I wanted to test its raw capability.
My Streamlined Approach
Using basic web-based audio editors, I kept my tweaks minimal: I trimmed silence and added clean fades, did some basic volume normalization, and a quick polish for streaming platforms. I did not re-record vocals, rebuild arrangements, or make any major structural changes. Why? I really wanted to answer a specific question: How close can AI get to professional quality without a lot of human intervention?
The Professional Touch: AI-Powered Mastering
Even the best raw recording needs mastering for streaming platforms. To complete my end-to-end AI pipeline, I used LANDR’s AI mastering service (LANDR, n.d.) to handle loudness optimization and spectral balancing.
What Actually Happened: The Results
The track that emerged genuinely surprised me. It delivered:
- Coherent vocal performances with emotional depth.
- A professional-sounding arrangement and mix quality.
- Genre-appropriate energy and dynamic shifts.
- Memorable melodic hooks and a logical song structure.
While it might not match the nuanced artistry of a top-tier human vocalist, the consistency and adherence to genre impressed me. More importantly, it sounded intentional rather than random.
Key Lessons I Learned
- Specificity wins: Vague prompts produce vague results. The more detailed your sonic vision, the better the AI can execute it.
- Iteration changes everything: You can’t expect perfection on your first attempt. Each generation teaches you more about communicating with the system.
- Structure guides success: Explicitly defining song sections helps the AI create professional arrangements (Songwriting.net, n.d.).
- Reference points work: Mentioning specific artists or styles gives the AI concrete sonic targets.
- Lyrical intent matters: Well-crafted lyrics that serve the song’s emotional purpose dramatically improve the final result (Stolpe, n.d.).
Key Insights for Creators and Musicians
For Musicians: AI isn’t a replacement; it’s a creative partner (ResearchGate, 2025). Success comes from learning how to communicate with these systems through strategic prompting. Think of yourself as a director, giving clear guidance to a highly capable session musician. AI can help with technical execution and overcome creative blocks, but your taste and vision are still the most important elements. It’s a powerful tool to prototype ideas and quickly hear what’s possible, even if you lack traditional skills.
For Content Creators: These principles stretch beyond music. Whether you’re generating written content, visuals, or audio, success lies in giving AI specific, contextual direction instead of hoping for a miracle. Iteration and refinement become your creative process.
For Everyone: We’re moving towards a hybrid creative process where human strategy guides AI execution. The future belongs to those who master this collaboration, knowing that while AI can enhance your abilities, it won’t make you an expert overnight.
What’s Coming Next: Acknowledging the Novice’s Journey
The track I created sounds polished to me, but as a newcomer, I honestly don’t know if it’s “professional” in the eyes of an industry expert. The point of this experiment wasn’t to prove that I’m a hidden musical genius but to show that AI lowers the barrier to entry for everyone. The main challenge for a novice using AI is this: you don’t know what you don’t know. I can only prompt based on my existing knowledge and taste, and AI can only guide me based on that input. It can’t fill the gaps of my inexperience on its own.
Now that the music exists, the real questions for my next phase are less about technical creation and more about the human element. My next experiment will explore AI-powered marketing and music video creation, but I’ll approach it knowing that a professional’s help would likely be needed to truly create a “hit.”
Questions driving my next phase include:
- How can AI be used to build an authentic artist brand around a song?
- What marketing strategies can be used with AI to get music noticed?
- Can AI create a compelling music video that genuinely enhances the listening experience
Next step: Video Creation
References for Article
- Cambridge Popular Music. (n.d.). Lyrical Architecture and The Song Structure.
- Catapult. (n.d.). Music Marketing 101: Why Lyrics?.
- LANDR. (n.d.). AI Mastering | Online Audio Mastering
- ResearchGate. (2025, January 24). (PDF) Artificial intelligence in music: recent trends and challenges.
- Songwriting.net. (n.d.). Songwriting Tips: 10 Elements of a Song.
- Stolpe, A. (n.d.). 3 Principles of Songwriting.
- Suno.ai. (n.d.). Suno | AI Music.
- The Mystic Keys. (n.d.). Why Do Some Songs Get Stuck In Your Head? Catchy Music | The Science Behind It.
