Stop Gambling With Prompts. Start Directing the AI.

Most AI music tracks sound amateur for one reason. Not lack of talent. Lack of specificity.

In this episode I break down the craft of prompting — what the AI actually responds to, and why vague inputs produce average output every single time. We cover the 3-word fix that transforms your results immediately: sub-genre, mood, and texture. These aren't labels — they're commands. Sub-genres load specific instrument packages. Mood words are harmonic instructions, not feelings. Texture shapes the mix character before you ever open a DAW.

Then we go through the 10 DON'T DOs — the most common mistakes killing AI music tracks. Semantic Sludge. Semantic Dilution. Negative phrasing that produces the exact thing you told it to exclude. Impressionistic prompts that read like poetry but give the AI nothing to work with. And the mindset traps that keep most creators stuck in a loop of starting over instead of directing forward.

Gambling means typing something vague and hoping. Directing means knowing the rules and applying them deliberately.

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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_00 0:30

Hello and welcome to the AI music revolution. I am your host, Josh Gilliland. Look, most AI music, the track sound amateur for one reason, and it's not lack of talent, it's lack of specificity. So there's a distinction that we believe in here at JGB's lab, and that's the gambler versus the director. Most people type something vague into an AI prompt, into uh Suno or Yu-Dio or Murika. And they hope. Type something vague, hit generate, and they hope. They hope that what comes out the other side sounds decent. That's gambling. Directing means knowing what commands the AI actually responds to. So in this episode, we're going to cover the three-word fix that transforms output in the 10 most common mistakes that are killing your tracks. So let's get into it. First section here, let's talk about why your tracks sound amateur. So when you give Suno or whatever your engine of choice is a vague prompt, it makes average decisions, average instrumentation, average structure, average everything. The results sound like AI music. They sound average, they sound generic. They do not sound like your music. So what do you do to fix that? There's a three-word fix, subgenre, mood, and texture. These are the three layers that turn a generic prompt into something that is specific. So let's talk about what exactly these three words mean. Subgenre. Think of the subgenre as a module loader. So if you are going into your Suno prompt and you put hip-hop, well, that's too vague. The AI will collapse into statistical averages. However, if you go into it and you say boom bap hip hop, now the AI knows specifically what module to load. So they load swing and break beats. If you type in trap, the AI knows. So then it loads 808s and hi-hat rolls. Maybe you say lo-fi, then the AI knows to load tape saturation and produce an unquantized feel. So you're not labeling by putting these in here, you're actually loading a toolkit. So think about that when you are creating music. What is the subgenre that you are creating? What is the toolkit that you are hoping or you are wanting or you are directing the AI tool to load? Let's talk about mood next. So mood words are harmonic instructions, not feelings. So if you type in sad, that tells the AI to use minor keys and slower tempo, descending melodic phrases. However, if you type in happy, what that will trigger is major tonality. So use these as levers on the harmonic foundation for your song. Now let's talk about texture, the third word. This closes the whole loop. So vinyl, crackle, warm analog production versus nothing. This shapes the mixed character, the mixed character. So think what is it that you want the texture to resemble inside of that song? How do you want it to feel from a texture standpoint? Vinyl, crackle, warm analog production. You can feel that texture, you can hear that texture versus nothing. So let's talk about some examples here: a before and an after example. So before, a sad hip-hop song with piano versus a better prompt, a boom bap hip-hop, melancholic and nostalgic, 75 BPM, jazz piano loops feature over dusty swing drums and an upright bass, vinyl, crackle, texture, warm analog production, instrumental only. So you can you can see how that prompt there brings in the subgenre, the mood, and the texture to really tell the AI what modules to load, how to approach the production, and how this song should feel. Then add a structure layer on top of it. Quiet intro, building the full chorus, stripped back bridge, explosive final chorus. Look, professional tracks have intentional arcs. Amateur tracks are flat from start to finish. So make sure that you understand the structure that you are adding to that song or that you want that song to bring forward. The last thing on this list, post-production. This is not optional. Suno will give you raw material. They will give you a raw wave file, they will give you raw stems. You must, as a producer, finish the job. The tracks that sound professional, they are all the ones that got worked on after generation. And so what does this mean for you? If you understand a DAW, then you're a step ahead of the game. If you don't, now would be a great time for you to learn how to use a DAW. There's a lot of different options out there. I'm actually working on a book, should be coming out here very soon, of Reaper. I use Reaper. Reaper is my DAW of choice. And so I'm actually going to write a book, or I'm in the process of writing a book, on how to utilize Reaper to do the best mastering for AI tracks, specifically tracks that come out of Suno, but I'll touch on the other platforms as well. But this is your best path forward, your most scalable path forward is to take the time to learn a DAW, to understand the mechanics of it, to understand the nuance of using a DAW to master AI tracks. So there, you learn those skills and you have them forever. There are other options as well. The next one is a little bit more expensive, but that is you can hire it out. You can pay somebody to do the mastering for your songs. This is actually some service that we offer here at JG Beats Lab. You can go that route. There's a third route. And this is using AI mastering tools that exist. We've tested those inside of the Red Lab Access. We've tested many AI mastering tools, and we compared them to human mastering, and it's not even close. But if your choice is to not master a song or to use an AI mastering tool, use the AI mastering tool. The worst thing you can do is not master your song at all. Okay, now let's go into. I have a top 10 list. People love top 10 lists, right? So this is the top 10 don't do's when it comes to creating your songs in AI. So this first section here is going to be related to vocabulary mistakes that occur inside of your prompts. So the first one, don't use generic parent genres. We just touched on this, but this apparent genre again is like hip-hop versus boom bap hip-hop. Subgenres are absolutely the way to go. Understand the subgenre of the music that you are creating and make sure you are instructing your AI tool to load that specific package for that specific subgenre. Parent genres genres will not get you there. Number two, don't stack conflicting genres. This is fun to experiment with because you never know what you're going to get. But if you were to do country plus techno plus classical, what that creates is semantic sludge. The AI will revert to generic pop rock and ignore most of your instructions. So make sure that you are stacking compatible descriptors, genre plus mood plus texture. Make sure these are compatible. Because if you put too much of a variety in there, the AI Suno will just revert to the mean, the most generic they can possibly think of is what will be produced. Number three, this is the last of the vocabulary mistakes. Don't write prompts over 100 words. If you go under 25 words, then you'll see a lot more model hallucination. If you can get into that 40 to 80 words, that's really a sweet spot. If you go over 100, it's semantic delusion. The model loses the thread and you know picks favorites and becomes very generic. So really make sure that your prompts, the words you use in your prompts, are intentional. Intentional, they're there for a reason and they work well together. So don't just fill the prompt with as many words as you can cram in there. And don't just go super short. Find that sweet spot. All right, let's talk about syntax mistakes now. So this is number four. Do not use negative phrasing. So if you say no drums, oftentimes that produces drums. If you say no vocals, oftentimes that produces vocals. Because the AI often time, oftentimes hears the noun. The word no is a weak modifier. So replace every exclusion with a categorical command, instrumental, instead of no vocals. So think through what that categorical command is for your prompts. Number five, don't write impressionistic prompts. A dreamy song about floating through the clouds is a poem. It's not a prompt. You have to think like a producer, not a poet. Give it architecture. Again, to go back to what we were talking about with the prompt size. Make sure that you the words that you are using inside of your prompt are intentional. Yes, it's great to be a poet feeding into this. Challenge is to take that poetry that is in your head that you have captured and then translate that into architecture that the AI can respond to in a way that is intentional. All right, number six. Don't treat mood words as feelings. And so, what do I mean by that? Mood words are harmonic instructions. Use them strategically. So, again, like we talked about, sad does not mean a song to make you weepy, it triggers the type of music that is being created. Alright, let's talk about mindset mistakes. Number seven, don't expect first take magic. Budget 10 to 15 generations per keeper track is probably a safe bet. The first attempt that you're doing is simply for calibration. It's not delivery. Create a prompt, listen to it, understand what did it just produce? Are there things that were significantly off? If so, figure out where you need to update the prompt. Don't expect the first one to be good out of the gate. And if you do think it's good, then maybe question what your bar is set at. All right, number eight, don't ignore the golden seed. So when you hear a song and there's elements in there that you love, lock it in and iterate from there. Starting over from scratch every time is backwards. Load it into your Suno studio. Find the spots that you like, replace the other ones, see what kind of variations you can come up with, but don't just throw it away. Number nine, don't skip post-production. So we touched on this in the first section. Even the best generation from Suno or Mureka or 11 Labs is raw material at best. It's not a finished product. So make sure your mindset is of that. This is raw material that then you, as the engineer, as the producer, will take to the next level of professionalism and make it a finished product. The last one, strategic mistake number 10. Don't use only one platform. Suno wins on organic band feel and speed. Mureka wins on vocal separation and DAW-ready stems. Know which tool wins for the job. Experiment. We have insider Red Labs, we have taken Suno, and we have taken Mureca, and we have taken Eleven Labs, and we have taken them through many different testing processes. And they all have pros and cons. There are things that each one of them does better than the others. So understand what is out there. Understand what these tools are capable of and when each should be used. Don't just be a Suno expert. Be an expert across many different platforms. Just add more tools into your toolkit. Okay, every one of these mistakes is a version of gambling. Vague prompts, negative phrasing, expecting magic right out of the gate. They all come from not knowing what commands the AI actually responds to. So directing, directing means knowing the rules and applying them deliberately. Unlock Suno Studio Edition at jgbeatslab.com is$8.99 and every technique covered for Suno in depth. You can also go into the Red Lab Access for the full research library and ongoing protocol reports in every book and every guide we ever publish. Thank you for listening, and uh we'll chat with you next time.