Style tags in Suno AI are text prompts (also called meta tags) that describe the desired style, genre, mood, instrumentation, and other characteristics of the generated song. They act as “directives” for the model, helping it understand what kind of music you want to create. In other words, instead of the AI defaulting to some generic style, you steer the creative process with specific words. This is essential for personalizing the music—the meta tags give you precise control over the song’s structure and sound.
Among all the meta tags, style tags are the ones that determine the genre and overall sonic character. With them, Suno AI can compose music in virtually any style—from pop and rock to jazz or electronic music. You can even blend multiple genres to achieve a unique sound. In practice, style tags let you tell the AI things like “rock ballad with jazz elements” or “electronic pop with a danceable beat.” Such phrases are intuitive, but the introductory “I want the song to sound like” is redundant—it takes up space in the style field without adding value. It’s better to begin directly with “rock ballad with jazz elements.” This saves characters and sharpens the model’s focus. As of 2025, the style field allows up to 1,000 characters, but it’s still wise to use them efficiently.
Suno AI will strive to follow these guidelines.
**Why they’re key:
Because the user describes the desired sound through tags, they become the primary tool for personalization. Style tags influence many aspects of a song—from genre and instrumentation, through tempo and mood, to vocal style. With the right tags, you can turn the same theme or lyrics into completely different songs—for example, once as a slow acoustic ballad and another time as an energetic electronic track.
Interpretation and Prioritization of Tags
Suno AI is trained to recognize the style tags you supply and shape the music according to them. When you list multiple tags, the model tries to blend all specified elements. Generally, the first tag carries the greatest weight.
Each time you submit a request, Suno generates two versions of the song:
Conservative version: Follows the order and logic of your tags closely.
Creative version: May use only some of the tags or improvise more freely.
This lets you compare a classic result with a more experimental outcome.
Suno also adapts to your individual feedback. By “thumbs up” or “thumbs down” on a song, you signal to the model whether you approve. The more you use these buttons in your Workspace, the better Suno tunes itself to your taste, increasing the chances of offering similar compositions in the future.
How the Model Understands Your Tags
Every tag—whether it’s a genre, an instrument, or a mood—acts as an instruction. When you enter multiple tags, the AI “reads” the list and interprets them as desired characteristics of the final track. For example, with the tags:
Pop, upbeat, acoustic guitar
Suno will aim to generate a pop song with a lively tempo and prominent acoustic guitar.
Tag Priority in Blending
Community observations show that tag order matters. The first tag in your list usually exerts the strongest influence—Suno strives most to satisfy that one. For instance, with:
relaxing, lullaby, airy, mysterious
the model will prioritize a calming tone. However, very dominant styles (e.g., metal) can sometimes stand out even if they’re not first. Still, as a rule of thumb, list the most important style first.
Combining Tags and “Internal Logic”
There’s no officially published formula for how Suno weighs tags, but experienced users report some empirical patterns. Influence appears to diminish for each subsequent tag—the first defines the core genre, the second adds a subgenre or nuance, the third contributes even less, and so on. (Unofficial hypotheses suggest a roughly 50% drop in weight per position.) While these figures aren’t confirmed, they underscore that not all tags are equal—put your most critical descriptors up front.
Neither Suno nor similar models like Udio reveal full details of their “internal recipe,” so users continue to experiment in order to refine their approach.
Dynamic Learning
Interestingly, Suno AI likely improves over time based on feedback. One user noted that when a track is “liked,” the model records which generations and prompts were approved and then more consistently reproduces similar results. This suggests tag interpretation can be fine-tuned over time—if many people successfully use a particular tag combination, the AI may get better at understanding it.
Because of this adaptability—and the lack of complete documentation—trying different tag variations (and sharing your findings with the community) remains the best way to discover how Suno “thinks” about its tags.
Syntax of Style Tags – Rules and Best Practices
Suno AI provides a dedicated “Style of Music” field where you list your desired style tags. Until 2025, this field was limited to 120 characters, so you needed to choose tags concisely. From 2025 onward, the limit has increased to 1,000 characters—allowing more complex, precise descriptions. Even so, avoid overly long lists that might confuse the model. Strike a balance between clarity and specificity.
When listing multiple tags, the standard practice is to separate them with commas. For example:
Pop, upbeat, female vocals, 120 BPM
Each comma signals a distinct characteristic. Some users omit the space after the comma (e.g. rock,fast tempo,electric), but readability improves if you include it. What matters is using clear delimiters so Suno can distinguish separate words and phrases.
Capitalization:
Tags are case-insensitive—you can write rock or Rock with the same effect. Many users capitalize genre names for readability, but it doesn’t change the model’s interpretation. There’s also no need for quotation marks or parentheses around tags in the style field—just list the words. (You might see square brackets used in guides—e.g. [Pop]—but that’s only for clarity in documentation, not in the actual prompt.)
Multi-word Styles and Punctuation:
Some style labels consist of multiple words—e.g. alternative pop, lo fi, hip hop. Write these as separate words (without commas) so they’re interpreted as a single tag. For instance, to specify a lo-fi genre, write Lo fi (or hyphenated as Lo-fi); either form is accepted. If you instead write Lo, fi, the model reads them as two unrelated tags.
Hyphens and ampersands are likewise preserved when they’re part of a genre name: use Pop-Rock if that’s the standard spelling, or R&B & Soul when appropriate. Subgenres like post-hardcore require the hyphen. In general, write each style exactly as you would in an English musical context.
Correct vs. Incorrect Spelling:
Minor spelling differences can sometimes affect results. For example, hip hop and hip-hop are usually both recognized, but it’s safest to use the most common form (Hip hop without a hyphen, per the supported-styles list). Avoid slang or niche subgenre names unless you’ve confirmed that Suno supports them. Instead of an extremely narrow label like “dark clowncore vaporwave,” try “experimental electronic with dark atmosphere”—the latter is more likely to yield coherent output.
Disallowed and Ineffective Inputs:
You cannot use real artist names as tags due to copyright limits. While some users append “-like” (e.g. “Drake-like”) or mention popular song titles to guide style, the platform will reject direct artist names (e.g. “Elvis Presley style” or “Beatles”). This prevents overly literal imitation and potential copyright issues. Instead, describe the sound generically—e.g. “50s rockabilly” instead of “Elvis,” or “British 60s rock band sound” instead of “The Beatles.”
Another common misstep is cramming too many tags into the field. Even with 120–1,000 characters available, it’s better to use 3–5 well-chosen descriptors than 10 scattered ones. Finally, make sure to keep style tags separate from your lyrics or theme text—use the dedicated style field for tags and the song-text field for anything you want sung.
By following these syntax guidelines, you’ll give Suno AI the clearest possible instructions for creating music that matches your vision.
Genre Blending
Mixing Styles:
One of Suno AI’s most powerful features is its ability to combine multiple genres into a single song. By listing two or more genre tags, the model will attempt to fuse elements from each. For example, tags like Jazz, Hip hop can yield a jazz melody with a hip-hop rhythm (think jazzy hip-hop in the style of Guru’s Jazzmatazz), while Metal, Opera might produce something akin to symphonic metal. Of course, results can vary, but you often get fresh, original sounds. Official guides and experts recommend experimenting with unexpected combinations—blending styles is how you achieve a truly unique sonic landscape. Suno AI effectively offers a “palette” of genres to choose from: you’re free to combine rock and electronic, or classical and hip-hop, all within the same style prompt.
How AI Executes the Mixes:
When you specify more than one genre, the model looks for common traits and transitions between them. Sometimes the song will sound like a hybrid throughout—e.g. the Pop-Rock tag delivers pop melodies over rock instrumentation continuously. Other times, Suno may alternate elements—perhaps starting acoustically (if you include Acoustic) and then dropping an electronic beat in the chorus (if you also list EDM). However, Suno does not currently guarantee section-by-section style control unless you use advanced features (see Scenes below). Often the resulting style is a sort of arithmetic mean of the tags—Reggae, Hip hop might sound like rap laid over a reggae rhythm.
Example Genre Combinations:
The community has tried countless pairings. Some popular, well-working combinations include:
Pop + EDM: Modern electro-pop suitable for the club.
Rock + Synthwave: Electric guitars mixed with synths for a retro-futuristic vibe.
Jazz + Lo-fi: Cozy, “riffing” instrumental hip-hop jazz foundation (commonly known as lo-fi beats).
Classical + Metal: Epic orchestral metal (e.g., in the style of Nightwish or Two Steps From Hell).
Blues + Trap: Blues guitar over heavy 808 bass and trap drums (an experimental fusion).
Practical Tips for Blending:
Tag Order Matters: As noted before, the first tag is dominant. If you want an even blend, try swapping the order. For example, Latin, Rap may yield a Latin song with rap elements, whereas Rap, Latin might sound like a rap track with Latin rhythms.
Limit Your Genres: Stick to two—or at most three—styles at once; too many can confuse the model.
Dramatic Sectional Shifts: If you want verses to be balladic and a chorus to be hard rock, this is hard to achieve in a single prompt. Suno doesn’t inherently understand “switch to this style here” unless you generate separate segments or use the Scenes feature (available to Pro users). For gradual transitions, you may need to generate sections separately and then use the Replace Section tool, or export both versions and mix them manually.
Genre-Specific Behaviors:
Some genres carry inherent tempos or vocal styles that can push the track toward one direction despite other tags. For instance, Ambient + Drum & Bass will likely be more laid-back than typical DnB, yet more rhythmic than pure ambient—fast drum patterns tend to dominate. Rare or niche genres might not sound fully authentic if Suno hasn’t seen enough examples. In such cases, pairing them with a more familiar style can help. For example, if you want “Celtic trip-hop” but the AI struggles, try generating a Celtic motif first, then a trip-hop base, and see how they merge.
Regional Genre Adaptations:
Suno also supports regional flavors. Tags like Romanian pop, Regional Mexican, or Japanese rock will prompt the model to generate a sound tailored to the respective cultural audience, using characteristic rhythms, instruments, and vocal styles.
Overall, blending genres in Suno AI is a creative playground—some of the community’s most fascinating tracks have emerged from surprising tag combinations. Don’t be afraid to include up to three distinct styles and see what Suno comes up with!
Adding Instruments via Tags
Instrumental Tags and Their Function:
In addition to genre, you can explicitly specify instruments in your style tags that you want to stand out in the arrangement. Suno AI supports a wide range of instrumental tags—e.g. Piano, Drums, Guitar, Cello, Synth, and more. By adding such a tag, you’re telling the AI, “Please include this instrument prominently in the track.” The model will then attempt to integrate that instrument—for instance, the Piano tag directs Suno to feature a piano melody or accompaniment as a key element.
Similarly, Drums indicates that the percussion should be pronounced and drive the song; Cello prompts the inclusion of cello (often lending a classical or emotional tint); and Synth asks for electronic synthesizer sounds as part of the arrangement.
How to Enter Instrument Tags
Simply add the instrument’s name as a word in your comma-separated tag list. There’s no need for extra phrasing—e.g.
Rock, Female vocals, Guitar
is sufficient. You don’t have to write “with guitar” or “guitar solo” (though you may if you wish); a single-word tag usually does the job. Be mindful not to overload the list with too many instruments: a prompt like
Piano, Guitar, Violin, Saxophone, Flute, Drums
may overwhelm the model—it might include only some of them or produce a cluttered sound. Instead, decide which instrument is most important and tag only that one; leave the rest to the AI’s stylistic judgment (or mention them in your lyrics if you want to be explicit, though usually the style tag alone suffices).
Impact on Style:
Instrumental tags can also indirectly shape the overall style. For example, if you set the genre to Pop but add the tag Acoustic, you’ll likely hear organic pop—perhaps acoustic guitars or piano instead of electronic elements. Tags like Acoustic and Electric are broader style indicators that primarily affect instrument choice: Acoustic suggests acoustic instruments (guitars, acoustic drums, etc.), while Electric or Electric guitar brings in electric guitars, amplifiers, and a more “electric” rock tone. So, “Acoustic, Folk” almost certainly yields a dominant acoustic guitar, whereas “EDM, Electric” produces synthesizer sounds and electronic beats.
Specialized Instrument Tags:
Orchestral Instruments: Tags like Orchestra or Orchestral prompt the model to orchestrate the music with classical instruments—strings, winds, timpani, and so on—as if backed by a full orchestra.
Synth Sounds: Beyond Synth, using tags like EDM (more a genre, but guaranteeing electronic instrumentation), Techno, or Synth pop brings in a variety of synthesizers.
Vocoder/Effects: While not strictly an instrument, adding Autotune as a tag (as some users have noted) can encourage a pitched/electronic vocal effect. Similarly, 8-bit may evoke retro video-game sounds if the model recognizes it.
Riffs and Solos: In the latest Suno versions there are even structural tags for instrumental moments—e.g. [Riff] or [Solo] in the lyrics can signal a spot for an instrumental solo. Experts suggest that “[Riff] works best when paired with an instrument tag like [Guitar Riff] or [Orchestral Strings] to add vitality.” You can therefore embed [Guitar riff] in your lyrics to cue a guitar solo. This moves into structural-tag territory—see section 9 for more on segment control.
Effect on the Output:
When you use instrumental tags correctly, you’ll notice that the generated song indeed features the requested instrument prominently. Users often praise how Suno has produced “an amazing guitar solo” when prompted with the right tag. If you don’t specify any instrument, Suno defaults based on genre—for pop it might choose synths and drums; for rock, guitars and a drum kit; for jazz, piano and saxophone, etc. Thus, use instrumental tags whenever you want to guarantee the presence or dominance of a particular instrument, or when you want to alter the genre’s typical instrumentation. For example, “Hip hop, violin” will generate a hip-hop beat with violin motifs (similar to popular tracks that sample orchestral strings).
Adding an instrument as a tag is an easy way to shape the arrangement. Suno AI understands many standard instruments—from Guitar and Piano to more exotic ones like Sitar or Banjo (if included in its training data). If you’re unsure whether the model “knows” a given instrument, try it—it will either ignore it or pleasantly surprise you by including it. Many creators use this feature to add unique color—such as an acoustic-piano version of an EDM track, or an electric-guitar lead in a classical composition. The possibilities are virtually endless, provided the instrument can fit coherently with the chosen style.
Tempo and BPM Settings
Controlling Tempo:
Beyond styles and instruments, Suno AI lets you specify your desired tempo. You can do this descriptively—using terms like “fast,” “slow,” or “mid-tempo”—or precisely by giving an exact BPM (beats per minute) value. For example, for a ballad you might add the tag slow or 70 BPM, whereas for a dynamic club track you’d use fast tempo or 140 BPM. The model recognizes these cues and will attempt to match the track’s rhythm and speed accordingly. Internally, Suno understands the concept of BPM well enough to follow it as guidance—e.g. “70 BPM” will set a slow pace around seventy beats per minute.
Syntax for Tempo:
To set tempo, simply include the number plus “ BPM” (e.g. 90 BPM) in your style tags. You can also combine this with a descriptor: for instance,
Tempo: slow, 70 BPM
Even just “70 BPM” is usually picked up as a tempo instruction, but adding “slow” or “fast” helps confirm your intent. Some users employ a bracketed format like [Tempo: 80 BPM] (mirroring other meta-tags), but in the dedicated style field you need no brackets—just list the tempo alongside genre and other tags. For example:
Rock, fast, 150 BPM
clearly tells Suno it’s rock, with a fast pace around 150 beats per minute. You may also use classical Italian tempo terms (Largo, Allegro, Moderato, etc.), though their interpretation can be less reliable—using numbers or simple English is safest.
Effect on the Generated Music:
When you give a specific BPM, Suno strives to match its drum patterns and overall pulse to that tempo. It won’t always hit the exact figure perfectly, but the feel is usually close. For instance:
60 BPM yields a very slow, languid rhythm—ideal for a slow beat or balladic counting.
120 BPM delivers a moderate, danceable tempo common in pop music.
160 BPM produces a high-energy pace, suitable for energetic rock or drum-and-bass.
Users report that the AI responds well to clear tempo instructions and structures the track accordingly.
Combining Tempo with Other Tags:
Tempo tags don’t exist in isolation—you’ll get better results when they align with genre and mood. For example:
For an “energetic metal” track, choose a high BPM and add tags like fast, aggressive.
For a “chill lo-fi” track, use a low BPM (e.g. 80) plus relaxed or laid-back.
These tags reinforce each other. You can also specify section-based tempo instructions—e.g. “slow verse, upbeat chorus”. One community example is:
which yields a track whose verses are calm and whose chorus bursts with energy.
Time Signature (Meter):
Although the question centers on tempo, Suno also accepts meter instructions. You can add 3/4 for a waltz feel or 4/4 for a standard rhythm. In advanced settings, some users specify time signature for precise control. This is a finer adjustment—beginners need not worry—but you can try tags like waltz or 3/4 time if you want a true waltz tempo.
Limitations:
While helpful, tempo tags don’t guarantee mathematically exact BPM—generated audio naturally fluctuates. If you need absolute precision (for post-production or synchronization), you may have to measure and slightly stretch or compress the track in audio software. For creative purposes, however, Suno generally follows “fast vs. slow” very well. Another limitation is that a song usually maintains one tempo from start to finish—it won’t automatically accelerate or decelerate (ritardando) unless the model chooses to for artistic reasons. To change tempo mid-song (for example, speeding up at the climax), you must describe it in your tags (with mixed success) or use the Scenes feature to generate separate sections at different BPMs and then combine them. That’s an advanced technique; typically you set one target tempo per generated song.
Tempo and BPM tags are a great way to tune the atmosphere—slow for emotional, romantic, or moody tracks; fast for danceable, uplifting, or aggressive tunes. Feel free to experiment with unconventional tempos—like 100 BPM for a mid-tempo vibe or 180 BPM for ferocious speed (as in speed metal). Suno will strive to follow your specified tempo and often does an impressively faithful job when given precise values.
Vocals and Specific Settings
Choosing Vocal Style and Voice:
Suno AI not only composes the music and instrumentation but also generates vocals—synthesized singing parts. The model can sing with different voices, genders, and even languages, as long as you give it direction. Through your style tags, you can influence which type of vocal the AI uses. Some of the most useful tags here are those for singer gender and age:
Male vocals and Female vocals will prompt Suno to choose a male or female voice, respectively. By default, the model decides on its own—genres often imply a vocal gender (e.g., rock → male voice; pop ballad → female voice). If you want to override that stereotype, explicitly include “female vocals” or “male vocals” in your tags. Users report that simply adding “female vocals” yields a distinctly female lead, and likewise for “male vocals.”
Boy and Girl indicate childlike voices—useful if you want a children’s chorus or a “little kid singing” effect. There are also Man and Woman tags, essentially duplicating Male/Female but emphasizing an adult voice.
Note: If you use a child-voice tag, ensure your lyrics and context match—e.g., a child singing about dark drama may sound odd. Technically, AI can do it if you instruct it.
Vocal Delivery Style:
Beyond gender, you can hint at how the singing should sound:
rap vocals → for rapped or spoken-rhythmic delivery rather than melodic singing. Even if your genre is Hip-hop or Rap and Suno would rap by default, adding “rap vocals” can reinforce or introduce rap sections into another genre.
spoken word, narration, Narrator, Announcer → to switch to spoken vocals for poetry or intros.
choir, choral → while not an official tag, including “choir” or “choral” in your style can sometimes produce backing choral vocals. E.g. “epic, choir” may add a choir behind the music (great for cinematic tracks).
duet → no dedicated tag, but by listing both “female vocals” and “male vocals,” Suno will sometimes assign verses and chorus to different voices, effectively creating a duet.
Vocal Characteristics:
You can also describe the voice’s timbre and style:
deep voice, raspy vocals, soft whisper voice, breathy, whispery, soft → for airy, intimate textures.
operatic vocals, powerful vocals, belted vocals, emotional vocals, soulful gospel vocals → to push toward operatic power or gospel-style runs and melismas.
While these aren’t strictly “supported” tags, Suno often recognizes them contextually and tries to deliver accordingly. For instance, a prompt with “female vocals, clear whisper voice, organ, harp…” yielded a soft, slightly whispered female vocal in one community example.
Special Vocal Techniques and Effects:
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Descriptive Tags and Additional Parameters (Mood, Dynamics, Atmosphere)
Mood Tags – The Song’s Emotional Tone
Beyond genre or instrumentation, it’s crucial to convey the mood or atmosphere you’re aiming for. Suno AI supports many mood-based tags—some even categorized as “Mood-Based” styles—such as Chill, Lo-fi, Party, Romantic.
Chill ensures a calm, relaxing track suitable for background listening. Party ramps up energy, yielding a rhythmic, fun vibe as if for a dance floor. Romantic adds lyrical warmth and an emotional ballad feel.
You can also use general adjectives like happy, sad, dark, epic, energetic, mellow, dreamy, spooky, aggressive, etc. The model reliably grasps these words and reflects them musically—for instance, dark produces minor-key melodies and lower registers; dreamy yields ethereal pads and echo effects; aggressive drives a more forceful rhythm and vocal delivery.
Dynamics Tags – Intensity and Contrast
Dynamics refer to the volume and energy variations across a song’s sections. Suno AI lets you suggest contrasts like “soft verses, powerful chorus” as part of your style description. Such instructions prompt the AI to arrange quieter, stripped-back verses and full-force, louder choruses—emulating professional “quiet-loud” dynamics (think Pixies/Nirvana).
Use descriptors such as:
soft, mellow, gentle, restrained for subdued passages
powerful, loud, explosive, intense for climactic sections
Or apply a global dynamic tag like high energy (driving drums, fast tempo, momentum) versus low energy (minimalist, slower pace). A community tip is to append “high energy, big band” to achieve a bright, full-sounding jazz-orchestra feel with lots of momentum.
Atmosphere and Context Tags
You can also suggest a setting or ambience:
live concert may introduce subtle reverb or crowd applause.
intimate acoustic creates a small-room, close-microphone feel.
cinematic leans toward broad orchestration and sweeping epicness.
ethereal lends lightness and weightlessness; haunting adds dissonance and mystery.
These aren’t formal tags, but Suno’s training on musical descriptions helps it respond meaningfully to such adjectives.
Additional Meta-Tags for Effects and Environment
While not strictly “style” tags, you can sprinkle in ambient or effect keywords to enrich atmosphere:
Rain sounds, birds chirping to layer nature soundscapes in ambient or nature-themed tracks.
Audience cheering, applause for a live performance vibe.
Telephone filter or old radio effect can be hinted at via text cues like “[Verse: filtered vocals]” or by using lo-fi, which often adds noise and color.
Interplay Between Mood and Genre
Suno blends all tags. For example, Death metal, romantic might yield crushing riffs with unexpectedly melancholic solos or lyrics. While AI can attempt playful contrasts—like Happy Doom Metal, which might try a major-key doom riff—it’s best to keep mood and genre compatible or save lyrical contrasts for the lyric field rather than style tags.
Example Descriptive Combinations
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Interaction Between Style and Structural Tags
Structural Tags in Suno:
In addition to style descriptions, Suno lets you specify the song’s form—i.e. its sections: Intro, Verse, Chorus, Bridge, Outro, and so on. You do this by placing those words (in English) either in your lyrics—e.g.:
—or via a “Structure” field if your interface offers one. Suno AI officially recognizes meta-tags like Intro, Verse, Chorus, and Outro, and community reports confirm that Bridge is also understood as a transitional section, even if not explicitly documented.
Separation of Concerns: Structure vs. Style
Style tags and structural tags address different aspects and work together rather than conflicting. Structural tags enforce form: they tell the model, “Follow this sequence of sections—make your intro, then two verses, then choruses, etc.” Style tags determine how each section sounds: genre, tempo, instrumentation, mood, dynamics, and so on.
Suno first honors your structural markers—creating a recognizable intro, repeating choruses, distinct verses, and a closing outro—then overlays your style directives across all segments.
Practical Example:
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Conclusion and Practical Tips
1. Be Specific—but Don’t Overload
Pick 3–5 tags that capture your core vision (genre, key instrument, tempo, vocal type, mood). E.g.
Latin pop, guitar, male vocals, 90 BPM, romantic
is clear and leaves creative room. Avoid dumping a dozen vague descriptors that dilute focus.
2. Order by Importance
• Suno weights the first tag most heavily.
• If genre is paramount, start with it (e.g. “Rock, …”). If tempo or mood is critical, lead with “Fast, EDM, …” or “Dark, Metal, ….”
• Arrange tags from most to least critical.
3. Observe Syntax and Limits
• Separate tags with commas and spaces; write popular styles correctly in English.
• Keep the style prompt under the character limit (120 chars pre-2025, 1,000 chars now). Truncated tags are ignored.
4. Combine Style and Structure
• Use [Verse], [Chorus], etc., in your lyrics (or a Structure field) and supply style tags.
• Structure gives form; style “dresses” each section appropriately.
• Without structure, Suno guesses form; without style, it guesses genre.
5. Leverage Community Wisdom
• Browse Reddit, Discord, YouTube—to find up-to-date lists of tags, successful prompts, niche combos.
• Check which genre spellings and subgenres others have confirmed working.
6. Embrace Iteration
• Treat Suno as a creative collaborator. If the first draft isn’t perfect, tweak tag order, swap synonyms, trim excess.
• Many power users spend credits experimenting—your own results teach you the model’s quirks.
7. Common Pitfalls & How to Avoid Them
Too generic or no style tags: Always include at least one genre or descriptive tag to avoid “random” outputs.
Conflicting tags: E.g. “EDM, acoustic” can confuse. Either pick one direction or clarify (“Acoustic instruments with EDM beat”).
Invented genres: “Intergalactic dub polka” will likely fail. Break it into understandable parts: “spacey atmosphere, dub rhythm, polka melody.”
Ignoring tempo: If you get an unwanted pace, add a BPM or “slow/fast” tag.
Expecting perfection: AI can surprise—sometimes delightfully, sometimes oddly. Use multiple generations or post-edit when needed.
8. Respect Model Constraints
• Suno is trained English-language music but can produce amazing music on each language. When using another language, style tags still apply, but vocals and nuances may vary.
• Don’t name-drop real artists or branded terms—these are filtered out. Instead describe their style (“’60s British rock band”).
9. Use Exclusions When Necessary
• The “Exclude styles” field (up to 200 chars) lets you ban unwanted elements: no guitars, no male vocals, no happy lyrics
• Handy when Suno repeatedly adds something you don’t want.
10. Enjoy the Creative Journey
Building prompts for AI music is an evolving craft. As Suno improves, your clear, well-structured tags will yield ever-closer matches to your vision. Experiment boldly, learn from each run, share your findings—and above all, have fun composing with your AI collaborator!
