In AI music generation, how you write prompts dramatically affects output quality. Based on 100+ comparative experiments, here are the systematic rules for prompts that actually work.
Basic Prompt Structure
Effective AI music prompts consist of these elements:
[genre], [subgenre], [emotion/mood], [tempo], [instrumentation], [reference]
Example: Effective Prompting
Bad: "sad song"
Good: "melancholic cinematic orchestral, 80bpm, strings and piano, Hans Zimmer inspired, emotional buildup"
The difference is specificity. AI models respond better to specific English music terminology than abstract descriptions.
Genre Specification Techniques
Layering Technique
Combining multiple genres creates unique sounds:
- "Lo-fi hip hop × Jazz Fusion" → mellow jazz hip hop
- "Synthwave × Classical" → retro-futuristic orchestral sound
- "Math Rock × Electronic" → complex-rhythm electro-rock
Improving Emotional Precision
Rather than expressing emotion directly, describe situations and scenes:
| Avoid | Effective |
|---|---|
| "sad" | "rainy afternoon, empty coffee shop" |
| "happy" | "summer festival, children playing" |
| "tense" | "chase scene, city at night" |
Summary
Prompt engineering is the art of communicating with AI. By combining specific music terminology, scene descriptions, and references, you can generate music that matches your vision.