Anyone who has spent time generating lyrics with AI tools has run into the same problem. Whether you use GPT, Claude, Gemini, or Suno's lyric model, the output keeps reaching for the same vocabulary: shadows, echoes, neon, fire, flames, dust, ashes, broken, phoenix. Different tools, same words.
This comes up constantly in r/SunoAI and other songwriting communities. A few quotes that show up week after week:
"I've tried the big three and all three of them just produce the same lines."
"I end up changing about 98% of it nearly every time."
"It likes lyrics for how they look on the page, which is not how lyrics work."
The pattern: people use AI to draft lyrics, get something that looks fine on the screen but feels generic when sung, and end up rewriting most of it. For casual users that wastes time. For songwriters who actually care about voice and imagery, it kills the workflow.
Where it goes wrong
Three problems show up over and over in AI-generated lyrics, and they're worth naming separately because they each need a different kind of fix.
The vocabulary collapses to a small set. Asking for "no clichés" in the prompt buys you one generation. After that, the model starts reaching for the next-closest cliché — silhouettes for shadows, embers for fire, whispers for echoes. The vocabulary shifts an inch but doesn't really change.
Sections stop doing their jobs. A verse should set a scene. A hook should land a single phrase that survives being repeated four times. A bridge should change something — perspective, time, speaker. Most AI lyric output gives you four stanzas of the same emotional temperature, all doing the same emotional work.
Vague prompts produce vague output. "A breakup song" or "trap song about heartbreak" doesn't anchor the model against anything specific. The cliché tokens are the path of least resistance, so that's what you get.
How SongLyricsLab handles it
SongLyricsLab doesn't take a single prompt and hand it to a model. It walks you through five steps, and each step targets one of the failure modes above:
- Understanding your idea
- Sketching directions for your song
- Writing the chorus hook
- Drafting verses with concrete images
- Removing AI clichés and tightening lines
Steps 1 and 2: from a feeling to a direction
Most people don't sit down to write a song with a fully-formed scene in their head. They have a feeling, a half-memory, a phrase they can't shake, an unresolved conversation. Turning that into a prompt that an AI can actually use feels like extra writing — which is the opposite of why they came to a generator.
The first two steps are designed for that gap.
Step 1 takes whatever seed you have — a fragment, a feeling, a sentence — and asks a few targeted questions to flesh it out. Where is this happening? Who is in it? What just changed? What hasn't been said yet? Nothing is mandatory. The more you fill in, the more specific the draft can be.
Step 2 sketches a few directions the song could take from there. If your seed is "regret about a relationship," the same situation can land on resolution, on stuck-ness, on quiet acceptance, on anger. You pick one and the prompt for the rest of the flow is shaped accordingly. If you don't know which one you want, picking one and seeing where it goes is faster than staring at a blank input field.
Steps 3 and 4: each section is written separately
Once the direction is set, the hook and the verses get generated as separate calls, with different instructions for each.
The hook prompt asks for a single repeatable phrase that pays off the setup. One line, not a paragraph. A claim that survives being repeated four times in a row.
The verse prompt asks for a concrete scene with at least one specific noun. Not "the night" — a specific room, a specific object, a specific moment. The verse plants something the hook can land on, instead of restating the same emotion in different words.
This is what's missing when you ask a model to "write a song about X" all at once. The model has no functional pressure on each section, so all four stanzas come out doing the same job. Splitting the call gives each section its own purpose.
Step 5: the polish pass
After the draft is generated, the last step takes another pass to clean up. It looks for the cliché vocabulary that AI lyric output tends to fall back on, and rewrites those lines. It tightens phrasing where the model padded — adjective stacks, throwaway connectors, the kind of filler that reads fine on the page but doesn't sing.
The polish pass isn't doing anything fancy. It's there because even with good per-section prompts, the model will sometimes default to shadows anyway, and it's cheaper to clean the output than to keep regenerating.
If you want to see the whole flow end-to-end, songlyricslab.com is the live version. No signup.
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