Unlock YouTube's Algorithm: The Ultimate Metadata Guide for AI Music

AI Music Podcast

In this episode, we explore the most critical and often misunderstood element of releasing AI-generated music: metadata. With AI tools enabling the creation of music at an unprecedented scale, the challenge isn't production; it's discoverability. We move beyond basic tags to uncover the specific metadata strategies that will make the YouTube Music algorithm work for you, not against you. Imagine an indie filmmaker spending days searching for the perfect soundtrack for a tense, atmospheric scene in their sci-fi short. They need something specific: "dark, ambient, drone, with a sense of unease." Your AI-generated track could be the perfect fit, but if it's only tagged as "Electronic Music," they will never find it. We'll show you how to embed the precise language of your target audience directly into your track's data, making it instantly discoverable. What You'll Learn: - How should you name your AI-generated tracks for maximum search impact? - What is the best strategy for the "artist name" field when using AI tools? - Why is tagging 'mood' and 'use case' more important than genre for YouTube's playlists? - Should you explicitly tag your music as "AI-generated," and what are the pros and cons? - How can you reverse-engineer search terms from your ideal listener or client? - What are the most common metadata mistakes that make your music invisible? - Is it better to be broad or hyper-specific with your genre and subgenre tags? - How can you use metadata to target lucrative sync licensing opportunities in film and gaming? Follow my YouTube: https://www.youtube.com/@chenran818 or listen to my music on Apple music, Spotify or other platforms: https://ffm.bio/chenran818