The Translation Trap and the Master Prompt
The first time I asked AI to write a Latin song it failed the culture test immediately.
It didn’t write like a Latin artist. It wrote like a corporate translator — flowery, generic, like someone drafted it in English and ran it through Google Translate. The language was technically correct and completely wrong at the same time.
It also had a bias problem. Ask for a “fun” or “powerful” Latin track and it defaulted to Phonk. Every time. Because Phonk is globally trending and the model just follows the data. Ask for regional slang to fix the authenticity and it overcorrected — drowning the lyrics in the same five words regardless of whether you were making a Peruvian Huayno or a Mexican Corrido. Zapateo. Zapateo. Zapateo.
It was a blunt instrument that didn’t understand nuance.
The fix wasn’t prompting harder. It was engineering constraints.
I stopped asking the AI to be creative and started telling it exactly what it couldn’t do. Banned words. Regional slang ratios. Instrument names in their native language — because left alone the model turns every flute into an Irish folk instrument.
The instrumentation problem was subtler than the language problem and took longer to catch.
I was listening to actual songs in each genre and comparing them to my outputs when I noticed something off. When I asked Suno to create a track featuring a Zampoña — a traditional Andean flute — it didn’t sound like the Andes. It sounded like an Irish festival. The model had so much Celtic folk music in its training data that any flute instruction defaulted to that sound regardless of what I asked for.
The fix was never using generic English instrument names. Ever.
Not “flute” — “breathy Quena and Zampoña playing a melancholic melody over a Rabel.” Not “violin” — “Rabel, rustic Andean fiddle.” The regional name plus the regional context forced the model away from its default associations and toward the actual sound I needed.
This became a hard rule across every genre. Mexican Guitarrón not bass guitar. Colombian Gaitas not woodwind. The more specific the instruction, the more authentic the output.
I built static guardrails, then layered genre-specific rules on top for each subgenre — Sonidero, Champeta, Salsa Caleña, Huayno.
One thing that helped was Gemini’s improved cross-lingual capabilities. Early on the only workaround was writing prompts in Spanish to force authentic output. Once the model got better at semantic mapping across languages I could give English instructions and get native Spanish output without the translation penalty.
The result was what I started calling the Master Prompt — a formal architecture built around one idea: the AI needed a persona, not just instructions. I made it a Master Lyricist for each specific genre. That single shift changed the quality of output dramatically.
It was still whack-a-mole. Fix one cliché, another appears. But the guardrails got tighter with each iteration until the output was consistently authentic enough to test with real audiences.
The laboratory phase was over. Time to find out if any of this actually worked.