The hard part of making AI music is not pressing the button. The hard part is knowing what to put in before you press it, reading what comes back with any discernment, and deciding whether to keep going or stop. Most people who call AI music "generic" aren't wrong — they just stopped too early in the process, or they started without enough clarity about what they were actually trying to make.
This is a walkthrough of the process I have run through several hundred times. It treats generation as iteration, not as a vending machine transaction. When it works, the output doesn't sound like a machine wrote it. When it fails, you'll know exactly which decision to revisit.
Decide what kind of song you actually want
Before opening any tool, sit with one question: whose experience does this song live inside? Not "what genre" and not "what vibe" — those come later. Start with perspective, then place, then the emotional center of gravity.
A simple frame for this:
A [WHO] doing [WHAT], the moment right before [TURNING POINT]. The emotion underneath is [FEELING], not [SURFACE FEELING]. Keep it [ONE TONAL WORD].
The distinction between surface feeling and the feeling underneath is not a writing exercise — it is a generator instruction. A song about "grief" sounds one way; a song about the specific irritation of being unable to cry at a funeral sounds like a completely different record. The specificity travels into the generation in ways that genre tags simply cannot.
While you're still thinking on paper, decide on length. A two-minute track and a four-minute track call for different structural choices, and the generator will drift without a target. Pick one before you move.
Step 1: write a prompt that names a posture, not a texture
Most first prompts describe sound: "lo-fi beat, warm keys, melancholic." That describes what the track should feel like to a listener three steps removed from the emotion. A posture describes what the performer is doing with their body and attention.
Compare these two:
- Texture prompt: "Slow R&B, soft falsetto, late-night, longing."
- Posture prompt: "Someone reading old messages they promised themselves they would delete. They keep reading. The vocal is quiet like they don't want anyone to hear."
Both point at a similar emotional destination. The posture prompt gives the model something to perform. The texture prompt gives it a sonic reference and nothing else. The results are not equivalent.
Keep posture prompts to three or four sentences. The ceiling is lower than you think — after about five sentences the model starts averaging across the instructions rather than building on them.
Step 2: pick a generator that lets you compare takes
Single-take generators make iteration slow in a specific, annoying way: you get a result, it's almost right, you regenerate with a tiny tweak, and the new take lands in a completely different direction because there was no shared anchor. You end up chasing the original take that was "almost it" for six cycles.
Running parallel variants solves this. AISongGen's music generator renders five takes simultaneously from the same prompt, so you can compare them side by side before committing to a direction. If two of the five are in the right territory, you have already skipped most of the regenerate loop.
A fair note: five takes cost more credits than one. If you have a very tight credit budget, run two takes instead of five and treat one as your reference. The point is to have at least one comparison, not to have five.
Step 3: write or co-write your lyrics first
The generator's lyric area is a small text field, and the model running behind it has a strong prior toward keeping whatever you give it — the original line count, the original rhyme scheme, even the original syllable pattern. If you write lyrics inside that field and decide later you want to add a bridge, you will fight the model on every regenerate.
Draft lyrics separately before pasting them in. The Lyric Studio gives you enough space to actually see what you're writing. You can revise a full verse, try a different chorus hook, move the pre-chorus before it becomes structural — all before handing anything to the generator.
Lyrics-first also lets you check one thing that the generator cannot: whether the lyric has a natural speech rhythm that a singer can actually land. Read your chorus aloud. If you stumble, the model will too.
If you're building the lyric interactively alongside the music — prompt first, refine lyrics second — that workflow is also valid. The key is that the lyric edit happens somewhere with real editing space, not in the generator's text box.
Step 4: choose your style controls with intention
Genre tags are seeds, not contracts. "Indie folk" does not lock the output into any specific production style — it biases the model toward a cluster of sounds associated with that label, which is a starting point, not a guarantee. If you want to understand how the model actually interprets these tags before committing, the guide on genre tags is worth ten minutes of your time.
What actually constrains the output more reliably:
- Mood, named precisely. "Bittersweet" and "resigned" land differently even within the same genre tag.
- Scene or setting. "Empty parking lot at midnight" gives the mix engineer (the model, here) a visual reference for reverb and space.
- Vocal gender and register. Most generators accept explicit instructions here, and the default is not always the right one for your lyric.
Set BPM if you know it. Not a range — a number. "Around 90" gives the model too much room. "88 BPM" gives it a clock. Same with track length: write the target duration explicitly rather than leaving it to the default.
Step 5: render, then listen on the worst speaker you own
AI-generated tracks have a known failure mode: they sound better on headphones than they deserve to. The stereo field is often wide, the low end is controlled, the mix is clean in a way that only reveals itself as artificial when you hear it on something unforgiving.
After the first render, move to your phone speaker. Or a laptop built-in. Or, if you have access to one, a car stereo with the windows down. These speakers collapse the stereo field, expose the low-mid mud, and surface the harshness in the upper mid range. If the track still sounds like a track — not necessarily good, but coherent — then you have something worth working.
If it collapses into mush, that is not always a sign to regenerate. It is a sign to look at your style controls. A low-end-heavy genre tag plus a warm room setting plus a slow BPM will often produce a track that does not travel. Adjust one variable, not all three.
Step 6: cover, re-render, or stop
Knowing when to stop is the skill that separates the people who ship from the people who have four hundred saved drafts and nothing on a playlist.
Three signals that a take is done:
- The chorus actually pulls. You feel the arrival before you think about it. If you have to reason yourself into why the chorus works, it doesn't.
- The vocal sits in the pocket. The singer sounds like they're singing this song, not demonstrating that they can hit these notes. AI vocals often over-articulate consonants — a good take doesn't.
- There are no AI-tells left that you notice on third listen. Drum patterns that are too metronomically clean. Chord transitions that lack any velocity variation. A held note that never breathes. These are the tells. One of them is often acceptable. Three is too many.
If the take clears two of the three, stop and call it a draft. If you clear all three, stop and call it done.
Re-rendering makes sense when one specific parameter is wrong and you can name it. "The vocal is too bright for the lyric" is a re-render instruction. "Something feels off" is not — that is a listening problem, not a generation problem, and more takes won't fix it.
Common mistakes
- Prompt too short. One sentence is not a prompt; it's a genre tag with a sentence wrapper. Three sentences is the minimum for a result with any character.
- Prompt too long. Eight sentences of detailed world-building gives the model too many constraints to satisfy simultaneously. It will average them and produce nothing in particular.
- Switching tools mid-iteration. Every generator has a different internal model, and "the same prompt" produces structurally different results across tools. If you switch mid-session, you reset your comparison baseline and lose the iteration history. Pick one tool per track and stay there.
- Regenerating with the same inputs and expecting a different result. The variation in outputs for identical prompts is real but bounded. If three consecutive takes are all wrong in the same way, the prompt is the problem, not the random seed.
- Ignoring vocal mismatch. The vocal timbre, register, and energy implied by your lyric have to align with the voice the model chooses. A lyric written for a raspy baritone delivered by a light tenor is a casting mistake, and no amount of re-rendering fixes casting.
After the first track that works
Download stems if the tool offers them. Even if you don't plan to mix, having the vocal and instrumental separated means you can re-voice later, or hand the instrumental to a real singer without starting from zero.
Save the prompt exactly as it was when it worked. Not the version you iterated through — the final version. Copy it into a notes file, a spreadsheet, anywhere that is not inside the tool itself. Most tools do not persist prompts across sessions in a form you can easily search. AISongGen's music library auto-saves your generation history and the prompts that produced each track, which reduces how much you need to manage this yourself, but it's still worth keeping your own copy of the prompts that produced your best results.
Log two things for each track that works: the genre-mood tag combination you used, and any posture phrase that felt generative. Over ten or fifteen tracks, patterns emerge — you'll find the tag combinations that fit your creative range and the phrasings that reliably produce something worth keeping. That log is more valuable than any guide, including this one.
If you want to see how other people are using the generator before committing to your own workflow, the reviews page shows how real users are approaching different genres and use cases.
The goal is not to generate music. Generating music is the easy part now — anyone can press the button. The goal is to write songs. Songs that have a perspective, a specific emotional center, a structure that earns its ending. AI is the production layer: it handles the arrangement, the mix, the voice. You still have to do the writing. The more of that you bring to the prompt, the less of it you hear missing in the output.