From Instrumental to Anthem: A Case Study in AI-Powered Music Production (backpocketmusic.com)

An artist used Suno’s Remix/Cover feature to breathe vocals into a back catalog of instrumentals, developing a repeatable “Suno - assist” workflow that pairs an LLM-driven prompt engineer with Suno’s audio engine. By keeping Suno’s “Audio Influence” high, the system leans on the original stems while applying AI-generated toplines, arrangements and production directions. Through three detailed case studies (ambient opener → radio-ready pop, minimalist piano → massive chorus, lo‑fi beat → spacious vocal-forward mix) the experiment produced release-quality results—one track reportedly took ~2,000 credits and the creator consumed ~25,000 credits over a few days—and even produced a memorable “happy accident” where a missing bracket caused the model to sing the prompt itself. Technically, the project hinges on precise prompt engineering: a two-part, machine-readable output (Style Prompt: comma-separated descriptors <1,000 chars; Lyrics: structural tags and square‑bracketed instrument/vocal/dynamic cues) that instructs Suno how to perform, mix, and arrange. Lessons include the power of high-quality inputs, using prompts to enforce syllable counts, dynamics, subtraction for space, and iterative refinement. Implications for AI/ML practitioners: AI vocals are now a practical production tool that accelerates ideation and release cycles, raises new cost and IP considerations, and may spark provenance or “proof‑of‑human” systems to differentiate fully human-made works.