AI Music Candidate Comparison Worksheet: Choose the Best Generation

Selection worksheet

Choose the strongest foundation—not merely the cleanest generation.

Score each candidate against the same project brief so novelty, preference and credit fatigue do not become the only criteria.

AI Music Master HubComplete Workflow

1. Label the candidates

Record the output link, file name, tool, model, date, prompt version and uploaded sources. Use no more than five finalists in one round.

2. Score five criteria from 1–5

Criterion 1 3 5
Emotional impact Little connection Some strong moments Immediate and sustained effect
Song identity Generic or confused Recognizable direction Distinctive and coherent
Vocal credibility Distracting or wrong Usable with repairs Believable and aligned
Structural strength Sections do not build Basic structure works Strong arrival and resolution
Edit potential Near-total rebuild Several fixable weaknesses Strong base with targeted changes

3. Complete the score sheet

Candidate Emotion Identity Vocal Structure Edit Total /25
A __ __ __ __ __ __
B __ __ __ __ __ __
C __ __ __ __ __ __
D __ __ __ __ __ __

21–25: strong candidate. 16–20: promising but requires work. 11–15: useful reference or partial salvage. 10 or lower: archive and move on. This is a decision aid, not an objective quality score.

4. Choose the next operation

FinishThe foundation serves the brief and needs targeted production.
CombineDifferent candidates contain complementary sections.
ExtendThe track needs an intro, bridge, ending or development.
RestartThe foundation fails the brief and repair would cost more than a new controlled direction.

Write the decision

Candidate ___ is selected because it best delivers ___, while the first production priority is to fix ___.

Save rejected candidates and rejection reasons to prevent circular decisions and support the contribution file.

Return to the AI Music Workflow HubDocument the Selection Record