How to Make AI Music in Any Genre: Prompt Recipes for Blues, Phonk, Lo-fi, Classical, and More

TL;DR

• Genre accuracy depends on two things: the right model and a specific prompt. Getting one wrong produces generic output
• Suno handles pop, lo-fi, country, hip-hop, and phonk well. Udio excels at electronic, jazz, blues, and anything requiring clean instrumental separation
• A prompt needs six elements: genre + subgenre, instruments, texture/production style, tempo, mood, and duration
• Stacking genre descriptors ("delta blues, slide guitar, 12-bar progression") outperforms single-word labels ("blues")
• Negative prompts ("no autotune," "no synths") are underused but prevent the AI from defaulting to modern production sounds
• If one model keeps missing your genre, try the same prompt on a different engine. Musci.io lets you test across Suno, Udio, Mureka, and others without switching platforms

Why Genre Matters More Than You Think

Type "blues" into an AI music generator and you will probably get blues-rock with a generic pentatonic guitar solo. The model has to choose between delta blues, Chicago blues, blues rock, rhythm and blues, and a dozen sub-genres — and without specifics, it picks whatever showed up most in its training data.

The fix is dead simple: be specific. "Delta blues, raw acoustic slide guitar, solo performer, foot-stomp rhythm, 1930s field recording feel" gives the AI a much narrower target than "blues."

Below are working prompt templates for seven genres, the AI models that handle each one well, and fixes for when the output misses.

Lo-fi Hip Hop

Lo-fi is one of the easiest genres for AI because the imperfections are the point. Vinyl crackle, tape hiss, and slightly off-beat drums are features, not bugs.

Working prompt:

Why this works: Lo-fi relies on texture words — "dusty," "warm," "lazy," "mellow." These guide the AI toward the right production aesthetic. Without them, you get clean digital output that sounds nothing like lo-fi.

Model recommendation: Suno handles lo-fi well out of the box. The model produces convincing vinyl textures and understands boom-bap drum patterns. Udio works too but tends to produce cleaner output that needs more texture descriptors to feel authentically lo-fi.

Common problem: The drums sound too crisp and modern. Add "tape-saturated drums" or "lo-fi crunch" to your prompt. If that fails, try "vintage SP-404 sampling aesthetic" — naming specific hardware associated with the genre helps anchor the AI.

More lo-fi prompts to try:

Phonk

Phonk is aggressive, bass-heavy, and built on Memphis rap aesthetics. AI handles it surprisingly well because the genre has strong, consistent sonic markers.

Working prompt:

Key elements that make or break phonk prompts:

• "Cowbell" — the signature phonk sound. Without it, you get generic trap
• "Pitched-down vocal samples" — the haunting, chopped vocal texture
• "808 bass" — heavy sub-bass, not clean bass guitar
• "130 BPM" — phonk typically sits between 125-140 BPM
• "Distorted" — the lo-fi grit that separates phonk from polished trap

Model recommendation: Both Suno and Udio produce solid phonk. Suno is faster and gets the energy right on the first try more often. Udio gives you a cleaner mix if you want phonk with more production polish.

Variation — drift phonk (slower, more atmospheric):

Blues

Blues is harder for AI than most genres because it relies on subtle expression — note bending, vibrato, dynamic phrasing — that AI models sometimes flatten.

Working prompt (Delta Blues):

Working prompt (Chicago Electric Blues):

Working prompt (Blues Rock):

Model recommendation: Udio produces better blues than Suno. The instrument separation is cleaner — you can hear individual guitar notes rather than a blurred wall of sound. Blues lives and dies on guitar tone, and Udio renders it with more fidelity.

Common problem: The guitar solo sounds generic and noodling. Add "pentatonic phrasing with note bending and sustained vibrato" to your prompt. Also try "call and response between voice and guitar" — this is a core blues structure that helps the AI produce more authentic arrangements.

Jazz

Jazz is where AI generators struggle the most. Improvisation, complex harmonic movement, the way a rhythm section breathes together — these are things current models fake rather than understand. You can get passable results, but do not expect Miles Davis.

Working prompt (Bebop):

Working prompt (Smooth Jazz):

Working prompt (Modal Jazz):

Model recommendation: Udio is the strongest option for jazz. Its cleaner mix helps with the instrument separation that jazz demands. Suno can produce passable smooth jazz but struggles with bebop's fast, complex passages. For classical jazz composition with proper harmonic structure, AIVA is worth trying — it was trained on thousands of scores and understands musical theory at a deeper level than general-purpose generators.

Common problem: Jazz output sounds like "jazz-flavored pop" — the harmony is too simple, the rhythm is too straight. Add specific harmonic instructions: "ii-V-I chord progressions," "tritone substitutions," "swing feel with ghost notes on the snare." The more music-theory language you include, the closer the output gets to real jazz.

The honest limitation: AI-generated jazz rarely sounds like real jazz musicians improvising together. It sounds like a competent arrangement performed cleanly. For background music, that works fine. For listeners who actually listen to jazz, the difference is audible.

Classical and Orchestral

Classical is broad — it spans Baroque counterpoint, Romantic symphonies, minimalist composition, and film scores. The prompt needs to specify the era and instrumentation precisely.

Working prompt (Romantic Symphony):

Working prompt (Baroque):

Working prompt (Minimalist):

Working prompt (Film Score):

Model recommendation: AIVA was specifically trained on over 20,000 classical music scores including Bach, Mozart, Beethoven, and Vivaldi. It understands symphonic structure — movements, dynamic arcs, counterpoint — at a level general models do not match. For orchestral and classical, it is the strongest option.

Udio is the general-purpose alternative if you do not want a separate AIVA account. It renders orchestral instruments cleanly and handles dynamic range (quiet to loud transitions) better than Suno.

Electronic and EDM

Electronic music is AI generators' home turf. The genre is already made by machines, so the gap between AI output and human production is razor-thin here.

Working prompt (House):

Working prompt (Synthwave):

Working prompt (Drum and Bass):

Model recommendation: Udio delivers more precise genre adherence in electronic styles. The clean mix works in its favor here — electronic music demands precise frequency placement, and Udio's 48kHz rendering handles it well. Suno produces good electronic tracks but the mix can sound slightly compressed compared to Udio's output.

Hip-Hop and Rap

Hip-hop has two distinct challenges: the beat (easier for AI) and the vocal delivery (harder).

Working prompt (Boom-Bap):

Working prompt (Trap):

Working prompt (Conscious/Lyrical):

Model recommendation: Suno handles hip-hop vocals more naturally than other general-purpose models. The vocal cadence, ad-libs, and rhythmic phrasing feel more authentic. For instrumental beats without vocals, both Suno and Udio work well. Mureka is worth trying for hip-hop beats if you want a lyrics-first workflow — you write the bars first, then the AI builds the beat around them.

Common problem: Rap vocals sound robotic and lose the rhythmic flow. Try adding delivery descriptors: "confident and rhythmic delivery," "conversational flow," "aggressive and energetic cadence." These help the AI choose the right vocal style from its training data.

Cross-Genre Experiments

One of the most interesting uses of AI music is blending genres that would be impractical to produce manually.

Working prompt examples:

These hybrids work because the AI does not care about genre boundaries. It treats style descriptors as mix-and-match ingredients. Sometimes the result is brilliant. Sometimes it is incoherent noise. Generate 3-4 variations and keep the one that does not make you cringe.

Musci.io is particularly useful for cross-genre experiments because you can test the same hybrid prompt across Suno, Udio, Mureka, and other engines. Different models interpret genre combinations differently, and the one that nails "classical trap" might fumble "blues phonk."

FAQ

Which AI model handles the most genres?

Suno v5 has the broadest genre coverage — it produces acceptable output in pop, hip-hop, country, folk, electronic, lo-fi, phonk, rock, and R&B. It handles less common genres (zydeco, klezmer, bossa nova) with mixed results. Udio matches Suno on most genres and surpasses it on jazz, blues, electronic, and classical where audio fidelity matters more. No single model is great at everything.

Why does my genre keep drifting to pop?

The AI defaults to the most statistically common patterns in its training data, which skew toward pop and rock. Fix this by stacking genre descriptors: instead of "jazz," write "bebop jazz, fast tempo, complex chord changes, saxophone lead, walking bass, ride cymbal." Redundancy anchors the model. Also add negative prompts: "no pop elements, no synth pads, no modern production."

Can AI produce authentic-sounding regional music?

It depends on the genre's representation in the training data. Mainstream global genres (pop, hip-hop, EDM) produce strong results. Regional genres with smaller training representation (Afrobeat, qawwali, Tuvan throat singing, Balkan brass) produce mixed results — the AI captures surface-level patterns but often misses the rhythmic and tonal nuances that make these genres distinct. For well-represented genres like bossa nova, reggae, and Bollywood, results are usually solid.

Should I write genre-specific prompts in English or my native language?

English produces the most consistent results across all platforms. AI music generators are primarily trained on English-language descriptions. You can request lyrics in other languages (and many models support 50+ languages for singing), but the style and genre instructions should be in English for best results.

How do I make AI music sound less "AI" in a specific genre?

Post-processing is the fastest fix. Run the AI output through a DAW and add effects that match the genre: tape saturation for lo-fi, amp simulation for blues guitar, room reverb for jazz, sidechain compression for electronic. These production touches close the gap between AI output and genre-authentic sound. Even 5 minutes of post-processing makes a noticeable difference.