AI-generated music should be judged by use, not novelty. A track can be technically interesting and still fail as background music. Before adding a track to a public library, it helps to run a simple checklist that focuses on real listening conditions.
First, check unwanted elements. If instrumental mode was selected, there should be no lead vocal, humming, spoken tags, or lyric-like phrases. If drums were minimized, the rhythm should not suddenly become dominant. If the prompt asked for cafe music, the result should not feel like a dramatic trailer.
Second, check musical stability. Background music should not wander into harsh dissonance unless the user asked for experimental tension. The key center should feel stable. The chord movement should support the mood. Sudden key changes, broken loops, or cheap General MIDI-like timbres should be marked as low quality.
Third, check utility. Can the track sit under speech? Can it loop or fade naturally? Is the volume reasonably normalized? Does the MP3 preview sound clear enough for web playback? Is the title short enough for a library card? These product details matter as much as the composition.
Finally, collect feedback. Likes, dislikes, comments, downloads, and play time are not vanity metrics. They are signals for the prompt engine. If users repeatedly dislike a certain prompt pattern, the system should learn to avoid it. Quality improves when every generation becomes a small test.