How do I make it generate something that doesn't sound like "AI music"?

Hey everyone! I've been absolutely obsessed with Suno for the past few months, using it almost daily to prototype ideas for client campaigns and even some personal fun projects. The sheer capability is mind-blowing. But, I keep running into the same wall, and I'm guessing I'm not alone: that telltale, slightly-too-polished, sometimes emotionally-flat "AI music" sound. You know the one—it can feel a bit like it's playing all the right notes but not always in the right *feeling*.

I'm trying to move beyond catchy jingles and generic background tracks. For instance, I wanted to create a raw, lo-fi indie folk snippet for a boutique brand's winter campaign, and despite my best prompts ("husky male vocals," "imperfect acoustic guitar," "tape hiss," "warm and intimate"), the output still felt a bit... synthetic in its perfection. The emotional resonance I was aiming for just wasn't quite there.

So, I've started a little personal experimentation lab to crack this. I'd love to compare notes with you all. What has worked? What hasn't? Here's where my head is at:

**On the Prompting Side:**
* **Specificity vs. "Vibe":** I'm finding "sounds like Band X meets Artist Y" often works better than a list of adjectives. Instead of "sad, slow piano," trying "in the style of early Bon Iver with a muted trumpet line like from a Miles Davis ballad."
* **Emotional Context:** I've started writing a short story or describing a scene for the song to inhabit. "A driver alone at 3 AM on a rain-slicked highway, grappling with a decision." This seems to steer it away from genericness more than just stating a genre.
* **Imperfection is Key:** Explicitly asking for "slightly off-tempo percussion," "a vocal take with breath sounds and a small crack," "unexpected key change in the bridge," or "background room noise."

**On the Post-Processing Side (My current deep dive):**
* I never just take the first output. I'm generating multiple variations and then often slicing, dicing, and mixing them in a DAW.
* Adding **real-world texture** is huge. I'll export the Suno track and layer in my own recordings—a real cough, the sound of a coffee shop, me tapping a pen on my desk, even running the whole thing through a cassette simulator VST.
* **Manual editing:** Sometimes just shifting the timing of a vocal phrase or introducing a manual "error" breaks the AI feel instantly.

My main hypothesis is that the "AI music" signature comes from a kind of statistical average of a genre. To break out, we need to force it away from that center point towards the edges, the quirks, the human mistakes.

What are your strategies? Have you found certain style tags or keywords that consistently yield more "organic" results? Are you doing any external processing? Let's pool our findings—I'm eager to learn from your experiments.

Happy testing!