AI Music Prompt Engineering by Genre (30 Templates)

AI music prompts are not magic words. They are pattern triggers that bias the model toward specific styles, instruments, moods, and production aesthetics it learned during training. Generic prompts like “upbeat pop song” produce generic outputs. Genre-specific prompts that use the right instruments, BPM, and production references for that genre produce outputs that actually sound like the genre. This guide gives you 30 tested prompt templates across the genres I use most in 2026, plus the universal formula that lets you build templates for any genre you do not see here.

I have generated several thousand tracks across Suno V5, V5.5, and Udio 1.5 testing prompts at scale. The templates below are the ones that consistently produce releasable results. They are not theoretical, every one of them has shipped tracks in my own work.

Quick Answer

The universal AI music prompt formula is: genre or subgenre + two to four concrete instruments + mood or emotion + BPM + one production reference. Example: “Dusty boom bap lo-fi, warm Rhodes piano, upright jazz bass, soft brushed drums, melancholic yet hopeful, 82 BPM, vinyl crackle texture.” For genre-specific templates, see the 30 prompts below organized by category. Use them in Suno V5’s Style field or Udio’s prompt input directly.

Key Takeaways

• The universal prompt formula is genre + 2-4 instruments + mood + BPM + production reference
• BPM is the single biggest tempo lever, set it explicitly when you have a target
• Concrete instruments beat abstract descriptors (write “Rhodes piano” not “warm keys”)
• Production references work when they match real recording styles the model has learned
• Negative prompts (style boost off, weirdness low) help on stylized genres
• Generate 3 to 5 takes per prompt because variation is the point of the model
• Iterate on the Style field, the Lyrics field shapes content not tone

The Universal Prompt Formula That Beats Random Generation

Before the genre-specific templates, here is the universal formula that all of them follow. Use this to build prompts for any genre, including ones I do not list below. The formula has five parts and the order matters less than the presence of each.

Genre or subgenre. Be specific. Not “pop,” but “synth pop” or “country pop” or “bedroom pop.” Not “hip hop,” but “drill” or “boom bap” or “Atlanta trap.” The more specific the subgenre, the more pattern the model has to anchor to.

Two to four concrete instruments. Not “guitar,” but “fingerpicked acoustic guitar” or “telecaster” or “12-string.” Not “drums,” but “808s” or “brushed snare” or “live kit with rim shots.” Concrete instruments give the model real arrangement targets.

Mood or emotion. Two adjectives is the sweet spot. “Melancholic yet hopeful,” “energetic and defiant,” “wistful and tired.” The emotion shapes the vocal delivery and instrumental dynamics.

BPM. Set it explicitly when you have a target. “82 BPM” for lo-fi, “120 BPM” for house, “140 BPM” for trap, “180 BPM” for drum and bass. If you do not set it, the model picks one that often does not match your song.

One production reference. “Vinyl crackle texture,” “tape saturation warmth,” “1980s analog production,” “modern Atlanta production.” References to real production styles bias the sound design toward those aesthetics.

Putting it together: “[Genre], [instrument 1], [instrument 2], [instrument 3], [mood1] yet [mood2], [BPM] BPM, [production reference].” That’s it. Every template below follows this formula.

BPM, Key Signature, and Time Signature When to Set Them

Before the genre templates, a brief note on tempo, key, and time signature controls. Each one has different reliability in Suno V5 and Udio 1.5.

BPM is the most reliable. Set BPM explicitly in your prompt as “82 BPM” or “120 BPM” and the model respects it within a few BPM. This is the single biggest tempo lever you have. Always set BPM unless you intentionally want the model to choose.

Key signature works but inconsistently. Writing “in the key of D minor” in your prompt biases the model toward that key roughly 70 percent of the time. It is not reliable enough to depend on for tracks that need to mix into other recordings, but it helps for solo tracks. If key precision matters, plan to transpose in your DAW after export.

Time signature beyond 4/4 is unreliable. Writing “in 3/4” or “6/8” or “7/8” gets the model right about 40 percent of the time. The other 60 percent of generations come out in 4/4 even when you specified otherwise. For non-4/4 work, expect to generate multiple variations and pick the one that landed in the right meter.

For more on Suno’s controls overall, see our how to use Suno V5 complete walkthrough.

Pop, Country, Folk Story-First Genres

Story-driven genres need prompts that prioritize vocal delivery and arrangement intimacy over production polish. Five templates below cover the most common subgenres in this category.

1. Modern Pop “Polished modern pop, layered synth pads, punchy 808 kick, programmed hi-hats, emotional and aspirational, 105 BPM, contemporary radio production.”

2. Bedroom Pop “Bedroom pop, dreamy reverb-soaked vocals, fingerpicked clean guitar, soft 808 kick, intimate and wistful, 92 BPM, lo-fi home recording aesthetic.”

3. Country Storyteller “Modern country ballad, fingerpicked acoustic guitar, lap steel, brushed drum kit, sincere and reflective, 78 BPM, Nashville production style.”

4. Indie Folk “Indie folk, fingerpicked nylon string guitar, upright bass, brushed snare, vulnerable and tender, 84 BPM, Bon Iver inspired production.”

5. Country Pop Crossover “Country pop crossover, acoustic guitar with electric guitar layers, four-on-the-floor kick pattern, defiant and uplifting, 112 BPM, Sam Hunt inspired production.”

Story-first genres benefit from imperfection cues in the Style field. Adding “intimate vocal performance” or “slight breath between phrases” produces vocals that land closer to human delivery. For more on humanization techniques, see how to make AI music sound less robotic.

For the country genre specifically, the cliches that AI defaults to (pickup trucks, dirt roads, small towns) need active blocking. Write the lyrics yourself rather than letting Suno generate them, and use structure tags to keep the verse-chorus structure in line with country conventions.

Hip-Hop and Trap Drum-First Prompts

Hip-hop subgenres need drum-first prompts because the rhythm section is the foundation of the track. Five templates below cover the most common subgenres.

6. Atlanta Trap “Modern Atlanta trap, hard-hitting 808 bass, crisp programmed hi-hats with triplet rolls, atmospheric synth pad, dark and confident, 140 BPM, Metro Boomin inspired production.”

7. Drill “UK drill, sliding 808 bass, sparse trap hi-hats with off-beat patterns, eerie pitched melody loop, aggressive and tense, 142 BPM, distinct UK production aesthetic.”

8. Boom Bap ”90s boom bap hip-hop, hard kick and snare on the 2 and 4, dusty sampled jazz piano loop, walking upright bass, nostalgic and reflective, 88 BPM, J Dilla inspired production.”

9. Lo-Fi Hip-Hop “Dusty boom bap lo-fi, warm Rhodes piano, upright jazz bass, soft brushed drums with off-grid timing, melancholic yet hopeful, 82 BPM, vinyl crackle and tape saturation texture.”

10. Modern Cloud Rap “Cloud rap, distant reverb-soaked 808s, sparse hi-hats, ambient synth pad, dreamy and detached, 130 BPM, Lil Uzi Vert inspired production.”

Hip-hop prompts benefit from explicit drum descriptors. The drum stem is what defines the subgenre. Boom bap drums are hard and on-grid. Trap drums are 808s with triplet hat rolls. Drill drums are sliding 808s with off-beat patterns. The model knows the differences if you name them.

For making rap beats specifically, our guide on how to make rap beats with AI covers the full beat-production workflow including lease structure and BeatStars distribution.

Electronic Subgenres House, Techno, Synthwave, Lo-Fi

Electronic music splits into many subgenres, each with its own production conventions. Five templates below cover the most common.

11. Deep House “Deep house, warm analog synth bassline, four-on-the-floor kick, off-beat hi-hats, smooth vocal samples, hypnotic and groovy, 122 BPM, Detroit house production.”

12. Synthwave “Retro 1980s synthwave, lush analog synth pads, arpeggiated sequencer, gated reverb snare, sub-bass pulse, cinematic and nostalgic, 110 BPM, Stranger Things production aesthetic.”

13. Future Bass “Future bass, bright supersaw chord stacks, sidechained pluck synth, half-time trap drums, uplifting and euphoric, 150 BPM, Flume inspired production.”

14. Techno “Detroit techno, driving four-on-the-floor kick, repetitive percussive loop, dark atmospheric synth pad, hypnotic and relentless, 128 BPM, minimal techno production.”

15. Lo-Fi Electronic “Lo-fi electronic, dusty drum machine, warm analog synth bass, ambient texture pad, mellow and introspective, 90 BPM, tape-saturated mixing aesthetic.”

Electronic genres respond especially well to BPM precision. Each subgenre has a narrow tempo range that defines it. House is 118 to 128 BPM. Techno is 120 to 135 BPM. Trap is 130 to 150 BPM. Drum and bass is 170 to 180 BPM. Setting BPM in the right range for the subgenre is essential.

Cinematic and Orchestral Hit Point Prompting

Cinematic music needs different prompt structure because the goal is often a dynamic arc rather than a song. Hit-point prompting tells the model where the energy should peak. Five templates below.

16. Epic Trailer Score “Epic cinematic trailer score, layered orchestral strings, brass swells, taiko drums, choir vocals, building from quiet to bombastic, 90 BPM, Hans Zimmer inspired production.”

17. Indie Film Score “Indie film score, fingerpicked acoustic guitar, sparse piano, sustained string pad, melancholic and reflective, 70 BPM, minimal production with room tone.”

18. Cinematic Drone “Cinematic ambient drone, sustained orchestral strings, low brass swells, slow ethereal pad, tense and foreboding, 60 BPM, Blade Runner 2049 inspired aesthetic.”

19. Documentary Underscore “Documentary underscore, sustained piano chords, sparse cello, soft brushed snare, thoughtful and contemplative, 75 BPM, transparent natural production.”

20. Hybrid Orchestral Electronic “Hybrid orchestral electronic, layered synth pads, orchestral string swells, electronic drum machine, futuristic and tense, 100 BPM, Christopher Nolan film score aesthetic.”

For cinematic work specifically, hit-point prompting means writing the arc into the prompt. “Building from quiet to bombastic” tells the model to start sparse and crescendo. “Tense and foreboding throughout” tells the model to hold a single emotional register. Pick the right arc for your scene.

For scoring an indie film specifically, our guide on how to score an indie film with AI music covers the full hit-point spotting workflow.

Genre Fusion Prompts That Actually Work

Genre fusion is where most prompts fail because writing “country and electronic” gets you mush. Fusion prompts need an explicit fusion cue that tells the model how the genres combine. Five templates below.

21. Country EDM “Country EDM crossover, acoustic guitar over electronic four-on-the-floor kick, banjo sample chopped into the drop, defiant and celebratory, 128 BPM, Avicii inspired production.”

22. Indie Trap “Indie trap, dreamy clean guitar over hard 808s, atmospheric reverb, half-time trap drum pattern, melancholic and detached, 130 BPM, Brockhampton inspired aesthetic.”

23. Lo-Fi Jazz Fusion “Lo-fi jazz fusion, dusty sampled jazz piano, electric bass with slap technique, brushed drums with hip-hop swing, sophisticated yet relaxed, 85 BPM, Robert Glasper inspired production.”

24. Folk Electronic “Folk electronic, fingerpicked acoustic guitar layered over analog synth pad, soft electronic drums, intimate yet expansive, 95 BPM, Bon Iver electronic-era aesthetic.”

25. Cinematic Pop “Cinematic pop, orchestral strings under modern pop production, layered synth pads, programmed drums, anthemic and emotional, 100 BPM, Imagine Dragons inspired aesthetic.”

For fusion to work, you must name both genres and add a cue for how they combine. “Country EDM crossover” works because “crossover” tells the model the country elements layer with the electronic foundation. “Folk electronic” works because the model understands the Bon Iver-era hybrid where acoustic and electronic sit together.

How to Iterate When the First Generation Misses

Even with the right template, the first generation often misses. AI models are stochastic, every generation produces a different take, and the right take is rarely the first one. Here is the iteration workflow that works.

Generate 3 to 5 variations of the same prompt. Suno gives you two variations per generation. Generate twice or three times to get four to six total takes. Listen to all of them.

Identify what missed. Is the BPM wrong? Adjust the BPM in the prompt explicitly. Is the genre slightly off? Add a more specific subgenre. Is the vocal delivery wrong? Add emotion descriptors or imperfection cues. Is an instrument missing? Name it explicitly in the instrument list.

Run the next batch. With the prompt refined, generate another 3 to 5 variations. Compare the new batch to the first. You should see the model trending toward your target.

Cherry-pick the best phrasing across takes. Sometimes verse 1 from take 2 has the best vocal, but the chorus from take 4 has the best instrumental drop. In Suno Studio (Premier tier), use section editing to combine the strongest sections from multiple takes. Outside Studio, comp the sections in your DAW after stem export.

Stop iterating when the track passes the eight-second test. Play the first eight seconds to someone who has not heard the track. If they react with engagement, ship it. Endless iteration produces worse results, not better.

For more on stem export workflow specifically, our guide on how to export Suno stems to Ableton, Logic, FL Studio covers the full DAW import process.

Five More Genre Templates for the Long Tail

To round out the 30, here are five more templates covering genres that come up regularly but did not fit neatly above.

26. Reggaeton “Modern reggaeton, dembow rhythm pattern, deep sub bass, Latin percussion, sensual and energetic, 95 BPM, J Balvin inspired production.”

27. K-Pop “Modern K-pop, layered electronic production, programmed drums with snap accents, anthemic chord progression, energetic and aspirational, 120 BPM, BTS inspired aesthetic.”

28. Ambient “Ambient soundscape, sustained synth pad layers, slow evolving texture, distant field recording elements, meditative and spacious, 60 BPM, Brian Eno inspired production.”

29. Heavy Metal “Modern heavy metal, drop-tuned distorted guitars, double-kick drumming, growled vocals, aggressive and powerful, 150 BPM, Architects inspired production.”

30. R&B Slow Jam “Modern R&B slow jam, warm electric piano, smooth bass, programmed drums with intimate swing, sensual and tender, 75 BPM, Daniel Caesar inspired production.”

That brings the total to 30 templates across the most-used genres in 2026. The universal formula at the top of this guide lets you build templates for any genre not listed here. The pattern is genre, instruments, mood, BPM, production reference.

For broader AI music context, our AI music trends that will define 2026 guide covers where the industry is heading.

Where Melodex Fits

I use Melodex to track which prompts produced which results across my catalog, so I can iterate on prompts over time and learn what works. Instead of losing prompts in browser history, Melodex keeps the prompt-to-track lineage organized. Sign up at melodex.app for an AI music workspace built around prompts.

External prompt resources worth bookmarking include SunoPrompt’s free prompt generator for additional template ideas and Travis Nicholson’s complete Suno prompt list for the broader prompt taxonomy.

Frequently Asked Questions

What’s the most important part of an AI music prompt?

The genre or subgenre specification. Generic genre tags (“pop,” “rock,” “electronic”) produce generic outputs. Specific subgenres (“synth pop,” “indie rock,” “deep house”) give the model real patterns to anchor to. After genre, BPM is the second most important lever.

Do production references actually work?

Yes, when they match real production styles the model has learned. “Hans Zimmer inspired production” works because the model has training data labeled with that style. “Sounds expensive” does not work because it is not a real production pattern. Use specific producer names, era references, or aesthetic descriptions when possible.

How long should an AI music prompt be?

The sweet spot is 4 to 7 comma-separated elements in the Style field. Below 4 and the model lacks direction. Above 7 and the model starts dropping elements that do not fit. Keep prompts tight and information-dense rather than long and aspirational.

Should I include lyrics in the prompt or write them separately?

For Suno V5 specifically, lyrics go in the Lyrics field, not the Style field. The Style field is for genre, instruments, mood, BPM, and production. Mixing lyrics into the Style field confuses the model. Keep them separate.

What’s the right BPM for each genre?

Lo-fi hip-hop is 70 to 90 BPM. Boom bap is 85 to 95 BPM. Country ballads are 70 to 90 BPM. Pop is 100 to 130 BPM. Trap is 130 to 150 BPM. House is 118 to 128 BPM. Techno is 120 to 135 BPM. Drum and bass is 170 to 180 BPM. Reggaeton is 90 to 100 BPM. Set BPM in the right range for the genre.

Do these prompts work on Udio too?

Yes, with minor adjustments. Udio’s prompt parsing is similar to Suno’s, accepting comma-separated style elements with instruments, mood, BPM, and production references. Some Udio-specific syntax for in-track inpainting differs, but the base prompt structure is the same.

Why does my prompt produce the wrong genre sometimes?

Usually one of three reasons. The genre tag is too generic (use subgenres). The BPM is in the wrong range for the genre (set BPM explicitly). Or the instrument list contradicts the genre (do not write “fingerpicked guitar” in a trap prompt). Refine the prompt to remove contradictions.

Should I use negative prompts?

Suno V5 supports a negative style box where you can specify elements to avoid. This works for blocking specific sounds you do not want (no auto-tune, no orchestral strings, no acoustic guitar). Use sparingly because too many negatives confuse the model.

How do I prompt for a specific vocalist sound?

Use V5.5’s Voices feature to clone a vocal identity, then attach the voice to generations. For prompt-only vocal direction without cloning, use descriptors like “male tenor vocalist,” “female alto vocalist,” “raspy delivery,” “smooth and breathy delivery,” “intimate close-mic vocal.”

Can I copy these templates directly?

Yes, every template in this guide is tested and ready to paste into Suno V5’s Style field or Udio’s prompt input. Adjust the BPM, mood, and production reference to taste, but the structure is ready to use.