How to master AI-generated music in a practical, release-focused way.

Step 1: Pick the release target before you touch anything

A master should always serve a destination. If you do not know whether the track is headed to Spotify, Apple Music, YouTube, SoundCloud, or a direct download, it is easy to make decisions that feel impressive but create friction later.

Choose the platform first so your loudness and delivery choices have a clear frame. That keeps the whole process practical.

Step 2: Solve the biggest audible problem first

Most AI-generated tracks do not need endless processing. They need one or two major problems fixed well. Start with the issue that most obviously hurts the listening experience, whether that is mud, harshness, unstable loudness, or a stereo image that feels weak.

A simple, focused pass is often better than trying to improve everything at once. The more specific the intervention, the easier it is to judge whether the track is actually improving.

Step 3: Compare at matched loudness and export cleanly

Always compare the processed file against the original at matched level. If the new version sounds better only because it is louder, your judgment is being biased by volume instead of quality.

Once the track feels cleaner, more balanced, and easier to trust, export the file in the format that fits your release path. Keep a consistent process if you are preparing more than one song for release.

• Choose the destination platform first
• Fix the most obvious problem before polishing
• A/B compare at matched loudness
• Export the right delivery format for release