The first time an AI-generated track made me feel something, I had to sit with that for a minute. It was a lo-fi hip hop beat from Suno, warm, slightly melancholy, with a piano loop that felt genuinely human. That was six months ago. The tools have gotten significantly better since then.
AI music generation has crossed a threshold. The outputs don't just sound "impressive for AI" anymore, they sound good. Good enough to use in videos, games, podcasts, and even releases. But getting there consistently requires technique, not just typing "make a sad song" and hitting generate.
This guide covers everything I've learned from generating over 500 tracks across every major AI music platform.
The Big Three: Suno, Udio, and AIVA
Suno: The All-Rounder
Suno is what most people think of when they say "AI music." It handles everything from country ballads to death metal to Afrobeats, and the vocal generation is eerily good. The v4 model introduced longer-form composition, full 4-minute tracks with verses, choruses, bridges, and outros that actually flow naturally.
The secret weapon is the style of music field. Most users dump everything into the lyrics prompt and ignore this field. Don't. Be specific: "melancholic indie folk, fingerpicked acoustic guitar, breathy female vocal, recorded in a bedroom" produces dramatically better results than "sad folk song."
Udio: The Audiophile's Pick
If Suno wins on versatility, Udio wins on audio quality. The productions sound more polished, more layered, more like actual studio recordings. Udio is particularly strong with electronic genres, the synth design and beat programming rival what you'd get from a producer with a decade of experience.
Udio's unique feature is the ability to extend and remix existing tracks. Generate a 30-second intro you love, then extend it into a full track while maintaining consistency. This iterative workflow feels closer to actual music production than any other AI tool.
AIVA: The Composer's Tool
AIVA targets a different audience entirely: people who need instrumental compositions. Film scores, game soundtracks, ambient backgrounds. No vocals, no lyrics, just pure composition. The output quality for orchestral and cinematic music is the best available, and the MIDI export means you can bring AIVA's compositions into your DAW for further refinement.
Prompt Engineering for Music (Yes, It's a Thing)
The difference between a mediocre AI track and a great one is almost always the prompt. Here's the framework I use:
The Five Dimensions of a Music Prompt
- Genre + subgenre: "synthwave" is fine, "dark synthwave with retro 80s production" is better
- Instrumentation: Name specific instruments: "Rhodes piano, fingerpicked nylon guitar, upright bass"
- Mood + energy: "Nostalgic but hopeful, building energy, medium tempo around 100 BPM"
- Production quality: "Warm analog production, vinyl texture, slight tape compression"
- Reference point: "In the style of Khruangbin meets Tame Impala"
A full prompt using all five: "Dreamy psychedelic soul, Rhodes piano and wah guitar over a deep funk bassline, relaxed and sun-drenched, warm analog production with subtle tape hiss, 95 BPM, Khruangbin meets Erykah Badu."
That prompt will produce something usable on the first try about 70% of the time. Without it, you're rolling dice.
Post-Processing: Where AI Music Becomes Professional
Raw AI output is a starting point, not a finished product. Here's the post-processing pipeline I use:
1. Stem Separation
Use tools like Demucs or Lalal.ai to separate the AI track into individual stems, vocals, drums, bass, and other instruments. This gives you mixing-level control over a track that was generated as a single audio file.
2. DAW Refinement
Import stems into Ableton, Logic, or FL Studio. Adjust levels, add EQ, apply compression. Replace weak elements, if the AI drums feel lifeless, swap them for real drum samples while keeping the AI-generated melody and harmony.
3. Mastering
AI tracks typically need mastering. They tend to be dynamically flat and slightly muddy in the low-mids. A simple mastering chain (EQ, multiband compression, stereo widening, limiter) transforms the output from "AI demo" to "release-ready."
Real-World Use Cases
Game Soundtracks
AI music is already standard in indie game development. Generate 20 tracks, pick the best 8, refine them, and you have a full game soundtrack in a weekend. Tools like AIVA produce MIDI that integrates directly with game engines for adaptive music systems.
YouTube and Podcast Background Music
This is the lowest-friction use case. Content creators need non-copyrighted background music in massive quantities. AI music fills this gap perfectly, and unlike stock music, every track is unique to your content.
Lo-fi and Ambient Playlists
The lo-fi genre is particularly well-suited to AI generation. The aesthetic already embraces imperfection, repetition, and ambient texture, all things AI music does naturally. Several popular lo-fi YouTube channels now use AI-generated tracks exclusively.
The Ethics and Licensing Question
Let's address the elephant: can you release AI-generated music? Legally, it's evolving. Suno and Udio both offer commercial licenses on paid plans. AIVA explicitly grants full commercial rights. The key is understanding each platform's terms and being transparent with your audience if you choose to be.
My take: AI music is a tool, like a synthesizer or a sampler. The human creative decisions (what to generate, how to refine it, where to use it) are what make it art. Use the tools. Make great music. Be honest about your process.