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February 2026 · By Jesse Meria
After generating over 1,000 tracks in Suno for Puana, I can tell you what separates a track you keep from a track you delete. It comes down to five things, and none of them are luck.
This isn't theory. I didn't learn this from a tutorial. I learned it by generating track after track, listening to the results, keeping the good ones, and figuring out what the good ones had in common. The patterns are clear once you've heard enough output.
The single biggest factor in Suno output quality is how specific your genre tag is. "Rock" gives you something. "90s alternative rock" gives you something much better. "90s alternative rock, grunge-influenced, distorted guitars" gives you exactly what you hear in your head.
Suno's model responds to layers of genre description. Each additional descriptor narrows the output space. Narrow is good. Narrow means the AI isn't guessing.
Generic
jazz
Specific
smooth jazz, late-night, warm saxophone, brushed drums, upright bass
I generate tracks for a cafe. "Jazz" could mean anything from free jazz to acid jazz to big band. "Smooth jazz, late-night, warm saxophone" means exactly one thing. That's what I want.
For a deep dive into genre-specific prompting, see the Suno Style Tags guide.
Most people write vocal direction like they're describing a character in a novel. "A soulful, experienced voice with hints of vulnerability and world-weariness." Suno can't do anything with that.
What works is short, specific, technical description. Suno responds to texture words, range indicators, and delivery style.
Too vague
beautiful female vocals
Actionable
breathy female vocals, alto range, intimate delivery
Too vague
powerful male singer
Actionable
raspy male vocals, baritone, raw and gritty delivery
Three elements: texture (breathy, raspy, clean, warm), range (alto, tenor, baritone), and delivery (intimate, aggressive, laid-back). That's it. Suno handles the rest. I use HookGenius to generate these because it has 14 vocal archetypes mapped to what Suno actually responds to, and I don't want to think about it every time.
If you don't tell Suno where the verse ends and the chorus begins, it guesses. Sometimes it guesses right. Mostly it doesn't.
Structure tags go in the lyrics field, not the style field. They look like this:
Every track I keep has structure tags. Every generic-sounding track I delete was missing them or had them in the wrong places. The correlation is close to 100%.
For the full list of tags Suno supports, see the Suno Metatags guide.
A great song has an energy arc. It doesn't stay at the same level for four minutes. Suno can create energy variation, but only if you tell it to.
"Building intensity" is different from "high-energy" is different from "laid-back." Combining these with section-specific direction gives you a track that breathes.
That prompt produces a track that starts quiet and builds. "High-energy indie folk" produces something that starts loud and stays loud. The energy descriptor is doing more work than most people realize.
Most Suno users skip this entirely. Genre, mood, vocals, structure — and then they stop. But the production layer is what makes a track sound finished versus demo-quality.
Adding a production descriptor to any prompt immediately makes the output more cohesive. It's the difference between a track that sounds like isolated elements playing together and a track that sounds like a record.
I generate roughly 5 tracks for every 1 I keep for Puana. That's after optimizing my prompts over thousands of generations. Early on, the ratio was closer to 20 to 1.
The improvement came from everything above: tighter genre tags, specific vocal direction, consistent structure tags, intentional energy descriptors, and production layer. Each one moved the ratio in the right direction.
I don't expect perfection from any single generation. But I expect the output to be in the right neighborhood. A great prompt gets you 80% of the way there. Suno's model handles the last 20%. Without the prompt doing its job, Suno is guessing — and AI guessing sounds generic.
If your Suno output sounds generic, the prompt is almost always the problem. Not the model. Not the version. The prompt.
You can learn to write better prompts manually. The guides on this site will teach you. Or you can use HookGenius and skip the iteration. Every generation applies the rules above automatically — genre-tuned style tags, vocal archetypes, structure, energy, production layer. That's what it was built to do.
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396 pages. 74 niche genre deep-dives. 40 artist profiles. Written by the team that has generated 10,000+ Suno tracks through the HookGenius pipeline since 2024.
Sample chapter · What the inside looks like
Genre · Boom Bap
Canonical references: Pete Rock, DJ Premier, J Dilla. BPM: 75-95 (90 is canonical). Drum signature: dusty kick, snare on 2/4, vinyl crackle under the loop.
Core prompt:
boom bap, gritty confident mood, chopped soul sample, dusty drum break, vinyl crackle, punchy male rap flow, classic New York feel, 90 BPM
Dark variant: swap “confident” for “menacing,” drop to 85 BPM. Bright variant: swap “gritty” for “uplifting,” raise to 95 BPM, add “jazz piano loop.”
One of 74 genre chapters. Every chapter is this concrete.
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