Troubleshooting AI Music Generation
Use this quick diagnostic page when outputs are weak or inconsistent.
Symptom: Results feel random every generation​
Likely causes:
- Prompt is too vague
- Too many conflicting descriptors
- No structure tags
Fix:
- Add exact genre + BPM
- Remove contradictory tags (e.g., “minimal” and “maximal wall of sound”)
- Include section flow:
intro -> verse -> chorus -> outro
Symptom: Groove is wrong​
Likely causes:
- BPM omitted
- Rhythm language too generic
Fix:
- State BPM explicitly
- Add groove terms: syncopated, half-time, straight 4/4, swung hats
- Add genre-specific drum hints (e.g., breakbeat, four-on-the-floor)
Symptom: Mix sounds muddy​
Likely causes:
- Overcrowded instrumentation
- Too much low-mid content
- Excess ambience descriptors
Fix:
- Reduce simultaneous layers in prompt
- Ask for cleaner arrangement and tighter low end
- Use controlled ambience terms (short plate, subtle hall, dry drums)
Symptom: No clear song structure​
Likely causes:
- Prompt only describes mood and timbre
- Missing arrangement cues
Fix:
- Add structural landmarks: intro, build, drop, breakdown, outro
- Specify section contrast goals (e.g., sparse verse, full chorus)
Symptom: Output lacks emotional direction​
Likely causes:
- Prompt gives technical tags but no emotional target
Fix:
- Add mood terms that align with the genre
- Anchor emotion to instrumentation and dynamics
- Keep emotional language consistent across sections
Fast Recovery Loop​
When a generation misses the target:
- Keep what worked
- Change one prompt variable
- Generate 2–4 candidates
- Compare against last best version
- Repeat until direction is stable