How to Make Song with AI?

Why AI Song-Making Went Mainstream (and Why Most People Do It Wrong)

Making a song with AI used to be a novelty. Now it's a business — and most people are doing it badly.

The numbers tell the story. The global AI music market sits at roughly USD 6.65 billion in 2025 and is projected to hit USD 60.44 billion by 2034, while Suno alone reports 2 million paid subscribers and $300M in annual recurring revenue. Listeners can barely tell the difference anymore: one market analysis found 82% of people cannot distinguish AI-composed music from human-made tracks.

Here's what most beginners do: they type one prompt — "sad indie ballad about heartbreak" — hit generate, and accept whatever comes out. If it's mediocre, they tweak two words and spin again. That's not songwriting. That's a slot machine with a monthly subscription.

The result is exactly what critics expect: a flood of forgettable output. As Trapital's Dan Runcie points out, most AI music is bad — but so is most of everything, and "the cream rises to the top in all formats." The difference between the cream and the noise isn't the tool. It's the process.

Berklee's faculty are blunt about this. Songwriting professor Ben Camp argues that "anybody can use AI to write songs" — so taste becomes the scarce resource: "If you don't have the taste to discern what's working and what's not working, you're gonna lose out to the people that do." His own workflow is anything but one-prompt-and-pray: he asks an LLM for ten verse iterations, pieces together promising fragments, critiques the result, and iterates before a single note is generated.

Professional producers follow the same discipline. The workflow that emerges across expert sources looks like this:

• Draft and refine lyrics with an LLM through iterative critique loops — not a single generation
• Generate audio with descriptive prompts covering genre, mood, instrumentation, tempo, and vocal style
• Edit at the section level using stems and regeneration rather than rerolling the whole song
• Polish in a traditional DAW like Ableton, Logic, or Cubase — where, as one producer guide puts it, "AI generates the raw material; your production skills transform it"

That four-step loop — lyrics, generation, editing, polish — is what separates a demo you'd actually release from a throwaway clip. And if you want to automate the surrounding grunt work like prompt drafting or project organization, agent skills can handle much of it; SkillzBench is building a free directory to discover, security-scan, and benchmark them so you know which ones genuinely help.

The rest of this article walks through each step with the specifics: exact prompting patterns, editing techniques, and the legal lines you cannot cross.

The Four-Step AI Song Workflow: From Lyrics to Polished Track

Every serious AI song workflow converges on the same four steps: write, generate, refine, polish. The tools change; the sequence doesn't. Here's how it works in practice.

Step 1: Draft lyrics with an LLM — and critique it back. Berklee songwriting professor Ben Camp asks an LLM for 10 different iterations of verses, pieces together the most promising snippets, and returns with specific notes like "the chorus needs more of a payoff" before iterating again. He then runs the sketch through an AI music generator to hear whether the lyrics are hitting, calling it "a very quick way to hear back lyric ideas." The critique loop matters because, as Camp puts it, anybody can use AI to write songs — the people with taste win.

Step 2: Generate the song with a descriptive prompt. Both Suno and Udio respond to prompts specifying genre, sub-genre, mood, instrumentation, tempo, and vocal style. Suno also supports metatags like [Verse], [Chorus], and [Bridge] to structure the arrangement, plus negative prompting to steer the model away from unwanted elements. Learning to write effective prompts is one of the most valuable skills in AI music production — treat it as a craft, not a slot-machine pull.

Step 3: Refine with stems and inpainting. Generation is rarely the end. Suno's Auto Split can break a track into up to 12 stems (50 credits), while Split from Mix costs 10 credits per stem. Udio's signature inpainting regenerates specific song sections without redoing the whole track. This is where AI output stops sounding like a demo and starts sounding intentional.

Step 4: Polish in a traditional DAW. Bring the stems into Ableton, Logic, FL Studio, or Cubase for mixing, mastering, and arrangement. As one production guide puts it, "AI generates the raw material; your production skills transform it into something truly polished and personal." Berklee professor Jonathan Wyner adds that AI-powered tools mean "the world's your studio" — but results still depend on understanding what makes music compelling.

A few things to keep in mind as you run this loop. Expect iteration: a rough cost analysis puts a kept Suno song at around $0.50 once you account for discarded generations. And don't outsource the fundamentals — Camp's advice is blunt: if you still need to learn songwriting, don't have the GPT write the song for you.

If you want to automate the surrounding busywork — prompt drafting, session notes, project organization — agent skills can handle those steps, and you can browse and test free ones on SkillzBench, which is building a directory with security scanning and benchmarking for exactly this kind of workflow automation.

• Ask for 10 verse iterations, not one "perfect" draft
• Structure generation with metatags and negative prompts
• Split stems before judging a mix — the raw output misleads
• Finish in a DAW; that's where taste becomes production

Choosing Your Tool: Suno vs. Udio (and What the Fine Print Says)

Two tools dominate AI song creation, and choosing between them matters more than most beginners expect — because the platforms have diverged sharply on what you can actually do with the music you make.

Suno's strength is workflow. Its browser-based Suno Studio is an "AI-native DAW" with timeline editing, BPM control, stem generation, and MIDI/audio export, and Suno's paid plans include commercial download rights — 20 downloads per month on Pro (~$10/month) and 60 on Premier (~$30/month). One detailed comparison puts the cost per kept song at roughly $0.50 when you use the full download allowance.

Udio's strength is sound. Reviewers consistently praise its vocal realism and granular inpainting — regenerating specific song sections — alongside stem downloads, remixing, style reference, and 30-second incremental building.

But here's where the fine print gets complicated, and why you should verify terms before publishing anything:

• One source reports that Udio disabled all downloads on every plan as of 29 October 2025 and revised its Terms on 12 November 2025 to restrict everything to personal, non-commercial use — no posting to YouTube, Spotify, or TikTok.
• Another comparison, checked at a different date, describes both platforms as commercially viable on paid tiers (Udio Pro at ~$30/month with commercial rights).
• Suno is described as the only one of the two whose songs can be downloaded and used commercially under current terms.

These sources conflict, likely because the platforms' terms changed between check dates. The practical takeaway: read the official pricing and terms pages the day you plan to publish.

Two other factors deserve weight. First, model quality is volatile. Suno's v5 (September 2025) was widely regarded as its best overall generation quality, but the V6 launch drew user backlash on r/SunoAI for "flat" and "muddy" mixes, with Suno recommending Max Mode — which costs more credits — for songs over two minutes. Whichever tool you pick, today's version quality is not a guarantee of tomorrow's.

Second, the legal ground has shifted toward legitimacy. Both platforms settled copyright disputes with major labels — Suno with Warner Music Group, Udio with Universal Music Group — which brought legitimacy to the AI music space and opened the door to licensing partnerships. Suno has since added deals with Warner, BMG, and Believe, while Udio settled with Universal, Warner, Merlin, Kobalt, and Believe.

The decision framework is simple: if you need downloadable, commercial-ready songs today, the evidence points to Suno; if you're prioritizing vocal realism and fine-grained editing for personal projects, Udio's toolset is compelling — provided you confirm its current terms first. And whichever you choose, remember the tool generates raw material; your taste and production skills determine whether the result is worth keeping.

Legal Lines and Human Taste: What AI Can't Do for You

AI can generate a melody in under two seconds — but it can't own one, judge one, or legally defend one. That's where you come in.

Under current U.S. law, works created by AI without human intervention cannot be copyrighted, according to Harvard Law lecturer Louis Tompros. If you want a protectable song, you need sufficient human authorship — creative selection and arrangement of AI-generated material. Your curation, editing, and structural choices are what transform raw output into intellectual property.

Voice mimicry is a separate, sharper risk. The fake Drake and The Weeknd track "Heart on My Sleeve" racked up over 9 million views on TikTok before removal following Universal Music Group claims. Imitating a style generally isn't infringement, but the strongest legal claim against voice mimicry is right of publicity — a doctrine with teeth dating back to Bette Midler's successful suit against Ford in 1988.

Your guardrails, in short:

• Don't publish purely AI-generated material and expect copyright protection — add meaningful human authorship through selection, arrangement, and revision.
• Never clone a real artist's voice, even as homage; right-of-publicity law applies regardless of copyright status.
• Verify platform terms before commercial use — terms change fast, and sources disagree on what's currently permitted.

The legal lines only cover half of what AI can't do. The other half is taste. Berklee songwriting professor Ben Camp puts it bluntly in a faculty discussion on AI in music: "If you don't have the taste to discern what's working and what's not working, you're gonna lose out to the people that do have the taste, because anybody can [use AI to write songs]."

That's the real differentiator. With 82% of listeners unable to distinguish AI-composed music from human-composed music, per market research on AI in music, generation is no longer scarce. Judgment is. Camp's own workflow keeps the human in the loop: he requests ten verse iterations from an LLM, pieces together the promising snippets, critiques the result, and iterates — AI is a sketchpad, not a ghostwriter.

The good news is that the surrounding grunt work — prompt drafting, iteration tracking, project organization — is exactly the kind of repetitive workflow AI agents handle well. Agent skills can automate those steps so your attention stays on taste and authorship. SkillzBench is building a free, security-scanned directory of agent skills, with benchmarking and a free MCP connector, so you can test which ones actually lift your workflow before trusting them. Browse and equip free, verified skills at skillzbench.com — and keep the judgment where it belongs: with you.