Your Track Isn't Done When Suno Is Done With It

Most AI music creators make the same mistake — they treat the raw export as the finished product. It isn't. What Suno hands you is raw material. What you do with it is where the real work begins.

In this episode:

The manifesto — why a raw export is not a finished track and what that gap is costing your catalog.

The 60-second diagnostic — four numbers that tell you in under a minute whether your track is ready for the next step or needs more work. Run this on every export before you touch another plugin.

Plus a clip from my conversation with Bob Sluys — a musician with fifty years of real experience who recently crossed over into AI music. Bob talks about how he discovered Suno through a decades-old songwriter's catalog, and what he said about Red Lab Access the moment he found it. Completely unprompted.

The full Bob Sluys interview is available now as Episode 1 of Red Lab Conversations. Link in the show notes.

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New episodes of the AI Music Revolution drop every Friday, and most Tuesdays. Everything mentioned in today's episode is at jgbeatslab.com. Links in the show notes.

Transcript

SPEAKER_01 0:30

Hello and welcome to the AI Music Revolution. I am your host, Josh Kellaland, the founder of JGB Slab. Your track isn't done when Suno is done with it, and that's the whole episode. That's the whole argument. A raw export is not a finished track. It is raw material. And the gap between what Suno hands you and what actually belongs on a streaming platform is exactly where directors earn their catalog. Today we talk about why that gap exists, what it costs you to ignore it, and the four-check diagnostic that tells you in 60 seconds whether your track is ready or not. Plus, a clip from my conversation with Bob Slaus, a musician with 50 years experience who is now deep into the AI music space. First up, we're going to talk about why your raw export is not a finished track. That's not an opinion, that's a production reality that most AI music creators are ignoring and it's costing them. Every track that goes straight from Suno to Spotify without a human hand on it is a missed opportunity. Today's manifesto is about why that gap exists, what it means for anyone serious about building a catalog worth listening to. Let's get into it. The difference between generating and directing. So there's a pyramid in AI music production. Most creators are at the bottom of that pyramid. There are a few climbing, but almost nobody is at the top. And here's what I want you to understand today. The top of that pyramid is not about having a better prompt. It's not about finding the right settings. It's not even about which platform you're using. The top of that pyramid is about what you do before and after Suno. Most people treat the generation as the finish line. Sunu spits out something, sounds pretty good, and headphones, they download the MP3, upload it straight to DistroKit or wherever, done. Released. Out into the world. That is not a finished track. That is a raw export wearing a release date. Here's the thing about the pyramid: the base, the very first layer above generic output is a purposeful prompt. Subgenre. Mood as a harmonic instruction. Texture as a production detail. That's where you get on the pyramid. That is the entry fee to the pyramid. But here's what nobody talks about with this. The button pressers, the people who click the magic arrow and download whatever comes out, they're not even on the pyramid. They're standing outside of it wondering why their music sounds like everyone else's. The answer isn't a better prompt. The answer is everything that comes after one. Above the prompt layer, you have personalized lyrics. Your words, your story, the human fingerprint the model cannot manufacture. Above that, you have a reference track, a sonic target. Not hoping the model lands, you know, in the right zip code, but actually giving it an address. Then you have identity control, an established persona that travels with every generation, a purposefully calibrated my taste profile, a fully baked custom model trained on your catalog. And then at the top of the pyramid is finished control. Arrange in Suno Studio. Make intentional decisions about the structure before the track ever leaves the platform. Fix tempo drift, remove effects that don't serve the song, shape the arrangement with intention rather than accepting whatever the generation produced. Pull it into a DAW. Mix at the element level. Add your own instrumentation. Make human decisions that no prompt ever could. And then master it. Loudness targeting, true peak limiting, artifact cleanup, streaming compliance. Do the full professional master. That is what separates a director from a vending machine operator. The generation did its job. The last steps are yours. Next up, we're going to talk about how to know what you're actually working with. The 60-second diagnostic. Four numbers that tell you everything you need to know about whether your track is ready for the next step or if it needs more work. Run this on every export before you touch another plugin. Let's get into it. So most people open Reaper and they go straight to the plugins. And that's backwards. Before you touch RIA EQ, before you add a single band, before you reach for the compressor, you need to know what is actually wrong with the track. Because the processing decisions you make in the next hour flow entirely from the answers to that question. So here's the diagnostic. Four checks, 60 seconds. Run this on every AI export before you do anything else. Check one. Listen for the low mid-hays. Jump to the chorus, the loudest, fullest section of the track. Focus on the vocals. Do they cut through cleanly or do they feel buried in a cloud? Does the mix feel open or slightly muffled? If it feels cloudy, you have low mid buildup. It lives between 250 and 500 Hertz, and it comes from the AI stacking multiple harmonic layers simultaneously. Your first RIA EQ move will be to cut in that range. Check two, listen for metallic shimmer. Focus on the symbols and the consonants. The S sounds, the T sounds, the hard edges. Is there a ringing, a fatiguing quality? Does a track feel tiring to listen to at moderate volume? If yes, you have upper mid artifacts. They live around five to eight kilohertz. This is the clearest hell of an unmastered AI track. And one of the most common findings across every project I've ever worked on. Check three. Jump to the final 10 seconds. Let the music fade. Listen carefully. What's left in the silence after the instruments drop away? Is there a clean decay into silence? Or is there something else there? A faint granular texture, a shimmer that doesn't belong to the music. That is the synthetic noise floor. It happens when the generative model runs out of musical material and fills the remaining space with predicted harmonic content. It needs reaffur treatment applied surgically to the tail only, not to the full track. Check four, run the dry run before anything else. Before you think about loudness targeting, go to file, render, and click dry run at the bottom. Dialog. Reaper processes your entire project in about one second and gives you a full scorecard. Peaks, clips, luffs I, love's S, and LRA without creating a single file. This is faster than inserting a loudness meter and playing the full track in real time. You get every number you need in one second flat. And here's what to look for. Peaks should be at negative one dB or below. If it's hitting zero or above, your limiter ceiling isn't holding. Fix it before you render anything. Clips should be zero. Any number above zero means digital distortion in the output file. Non-negotiable. Love's eye is your integrated loudness. Spotify targets negative 14. Apple Music targets negative 16. Note the gap between where you are and where you need to be. That tells you how hard Rhea Limit needs to work. LRA is your loudness range. Below three means the source is already heavily compressed and dynamically flat. Above 14 means dynamic variations are significant and that needs managed. Most genres sit comfortably between five and ten. Run the dry run first, know your numbers before you build the chain. That's the whole diagnostic. Four checks, 60 seconds. Low mid-haze, RIA EQ cut around 320 Hz. Metallic shimmer, RIA EQ cut around 2.7 kilohertz. Dynamic control with RIA XCOMP if needed. Synthetic noise floor, RIA fur as an item FX on the tail only. Loudness gap, RIAT limit to streaming targets, sealing at negative one dB. The processing decisions write themselves once you know what you're solving for. The mistake most people make is reaching for the tools before they know what the problem is, and ending up with a chain that sounds busy without sounding better. Ears first, plugins second. If you want the full mastering chain, every plugin, every setting, the complete diagnostic workflow, that's exactly what Unlock Reaper Mastering AI Music covers. It's 899 at jgbeatslab.com. And if you're a Red Lab Access member, it's already in your library waiting for you. Finally, let's hear from Bob Slaus. Bob has been in music since the mid-70s. A trumpet player, a bass player, a musical director on the Vegas strip, decades of real experience with real instruments. And he came to AI Music the same way a lot of you did, kicking and scratching against it. This is a clip from my conversation with Bob that dropped this week as the first episode of Red Lab Conversations. If you want the full story, the Roy Clark years, the Vegas strip, what he's building now, go find that episode. Links in the show notes. But this clip has two things I wanted you to hear. How he got into Suno through a songwriter's catalog that had been sitting in a drawer, and what he said about Red Lab access completely unprompted the moment he found it. Let's get into it.

SPEAKER_00 13:44

Many years ago, I did a demo for a young girl, and she was a precocious songwriter, 10, 11 years old. And anyway, I stayed in touch with her, and she ended up marrying a guy that had written Paula Abdul's hit song straight up. His name is Elliot Wolf. He also wrote Cold Hearted Snake. So he was a very established, prominent songwriter, had many hits. And they got married, they lived in Santa Fe, well, about 10 years ago. He passed away. And so now she's sitting on this huge catalog of songs that he had written that had never seen the light of day. You know, and she reached out to me and said, Hey man, I, you know, I'm sort of grieving and I'm, you know, and I don't know what to do. And I'm her Uncle Bob. And, you know, can you help me get these things figured out? And so over a process of some years, I did that. I liquidated his studio and you know, and then recently, a few months ago, finally, after all these years, she reached out and said, Okay, I'm ready to move forward and let's see what we can do with these songs. And some of them were from the late 80s, 90s, whatever. They were well recorded, they were masters, they were ready to be released, but you know, they were 30, 35 years old. And one day she calls me up all excited. She's on this thing called Suno. Suno doing, I thought those were Japanese guys wearing diapers, you know, rolling around on the that wasn't funny, but I couldn't wait to use that one. But uh anyway, so Suno and uh so I got out of the Suno crackpipe just instantly, and she was taking some of Elliot songs and running them through Suno. And so I went ahead and ordered the Suno thing as well. And I was for the last couple of years, was just kick and scratch and clawing anything against AI. I thought it was going to destroy mankind, and especially for music, that some kid could just sit there and type in some words, and two minutes later they got a hit song, and which actually has happened on on Spotify and some of those places, as I understand it, that you know there's some of that out there. Um, so that's sort of how I got into the AI thing. And when she turned me on to Suno, I bought it, and within minutes, your JG Labs ad started coming through my phone.

SPEAKER_01 16:00

Perfect, good.

SPEAKER_00 16:01

Nice, nice job there in marketing. And so, of course, everyone that's listening, first thing, the smartest thing I did since getting Suno was to subscribe to Red Lab and access and get all this wealth of information that I'm sucking up to uh Josh right now, but uh but it's it's incredible information, and I am now overnight a proponent of AI because I recognize it just as I did with drum machines and synthesizers as a tool that there's still the human component. And that's my take on it, and there's my history.

SPEAKER_01 16:36

I love your story. You know, you've been around music and making music for decades. And to come to the realization that AI is a tool. If you were to talk to your peers in the music space who took you know similar journeys to you, uh, I'm assuming you'd get probably a bit of pushback about how you know AI is cheating. Uh, but I'm glad that you were able to make that leap, you know, and come over to the dark side.

SPEAKER_00 17:08

You know, it's funny you use that term because that's exactly how I described it. When I did reach out to, I've got a good friend whose uncles were Monk Montgomery and Wes Montgomery, two of the early 50s, 50s. One of them was the first one to ever play a fender P bass on a recording. And Wes Montgomery might have been one of the finest uh jazz guitar players of all time. And he's about my age. And I I told him, I said, hey man, I went I went over to the dark side. And um and you know how I look at it, uh Josh, partly is not as much of a creative tool per se, because the way I'm using it, I've yet to do a song where I'm just typing in prompts, okay? I'm taking existing songs that were human-driven by excellent songwriters, and I'm getting a$10,000 master quality demo in two minutes, right? So I feel bad for someone who's running their little home demo studio on their laptop and charging$50 a song, like I used to do. I did that for years in LA. I do demos for people, you know. Uh take me a whole day, I'd lay down some bass and punch in a drum beat and and do the vote, you know, and then here's my hundred dollars and there's your demo, and you're never gonna get a record deal because no one's gonna listen to your cassette, you know. Yes, exactly. But but now, you know, with all the platforms that are out there, and so I think anyone that's listening to this, if they're either using it already or considering it, it it's a fantastic tool. You know, um it it's and we're training it how to how to get better, you know. There's dealing our content and using that to even improve the quality of of the AI machines.

SPEAKER_01 18:52

People who are taking artistic visions and feeding that into the tool, to me, that's superpower. You know, the thought that these tools, tools like Suno, are simply prompts and easy buttons and royalty checks. I mean, that's just not the reality of how we are using the tool.

SPEAKER_00 19:18

Let me uh let me jump in and comment, comment on that if I may. I I've only had it for five, six, seven weeks, whatever it's been, not even that, whatever. I have yet to scratch the surface. I I the sliders and the this and the I haven't even touched that stuff.

SPEAKER_01 19:34

Yeah.

SPEAKER_00 19:34

Here's something I did that was kind of cool. You know how typically in music or mixing or mastering or whatever, we always A-B, right? We just we listen, we compare the two, blah, blah. So here's what I did, and it kind of pissed off my my partner on this project I'm doing right now, because he's kind of prolific. He he's got his phone, he's got a keyboard and his voice, and he'll sit down and he'll have written his lyrics, you know. So he'll he'll have written a new song, and not one from 30 years ago, just boom, here's a new song. And he'll sing it, and his singing's decent, but it's certainly not in tune, and the phone's laying on top of the piano, and you can hear the cars driving by the neighbors, the dog is barking, right? And so you feed it in, and it comes out sounding like uh Queen's album, you know. And so here's what I've done on a couple songs, and he said, You gotta you can't show this to anybody, and which I haven't as a courtesy to him, but I will load his original demo, his MP3 from his voice recording off his phone. I'll I'll put that into my garage band, and then I'll also put in the the final Suno uh uh uh version and I'll I'll let him go for the first intro and the going into the first chorus, and then I'll morph it crossfade, right? Uh for a few measures, whatever, as it now you know goes from black and white to color kind of thing. And so it's like one-stop shopping of the comparison of you know, yeah the you know, as it you know, morphs into this, you know, version. And uh I I think it's really cool just to sh, you know, if nothing else, to show what what you get.

SPEAKER_01 21:20

That's Bob Slaus. 50 years in music, a few weeks in AI music creation, and already thinking like a producer. That's what happens when real musical knowledge meets the right tools. If today's episode hit home, if you recognize yourself in the diagnostic or Bob story, or in the gap between what your track sounds like and what it should sound like, Red Lab Access is where we go deeper. The books, the research, the blueprints, the community of directors doing this work every day. One price, lifetime access, everything included now, and everything we add into the future. jgbeatslab.com slash red hyphen labhen access. Link is in the show notes. Red Lab Conversations drop every Tuesday. The AI Music Revolution drops every Friday. Subscribe so you don't miss another one. And remember, the track isn't done until you say it's done. See you next time.