Chapter 4: The System Seeing the gap in 33 LatAm markets, building the Latin Fusion Engine, why you separated the GPTs, the production pipeline end to end.
Chapter 4: The System — Building the Assembly Line
Mapping 20+ LatAm markets was the easy part. The hard part was figuring out how to actually produce content across all of them without losing quality or losing my mind.
My first instinct was to build one master GPT that did everything — write the lyrics, generate the video prompts, produce the SEO metadata. One prompt, one output, done.
It broke immediately.
When you ask a single model to context-switch between writing culturally authentic poetry, describing visual scenes, and formatting search metadata, it gets confused. It starts guessing. The lyrics would be good but the video prompt would drift. Fix the metadata and the lyrics would break. Every time I touched one variable it destabilized something else. And processing one complete asset end-to-end took too long to ever scale.
The solution wasn’t a better prompt. It was splitting the work.
I tore down the master GPT and built three separate specialized ones — The Lyricist, The Creative, and The Metadata. Each one does one job and does it well. The Lyricist doesn’t know anything about video. The Creative doesn’t touch SEO. They don’t interfere with each other.
That separation changed everything. Instead of one asset at a time I could batch ten songs through the Lyricist simultaneously, then feed all ten into the Creative GPT at once. The assembly line was born.
On quality — because scale without quality is just noise.
Producing fast doesn’t mean producing slop. Culturally tone-deaf content gets rejected immediately by real audiences and the algorithm penalizes it fast. So I built a two-layer quality check.
The first layer was a dedicated QA GPT whose only job was to audit Lyricist outputs — flagging forced slang, cultural mismatches, hallucinated references. The second layer was my own review. Because the modular system standardized the outputs, anything off stood out immediately.
When something slipped through — and occasionally it did — the protocol was simple. Pull it down, fix the prompt, re-upload. No exceptions.
The soul of each track isn’t in the generation. It’s in the blueprint. The Master Prompt locks in the cultural corridor — exact instrumentation, regional slang ratios, banned words, specific genre rules. The AI executes variations within those boundaries. The authenticity doesn’t degrade at scale because the constraints never change.
Three hundred assets later, that system held.
Chapter 4: The System — Building the Assembly Line
Mapping 20+ LatAm markets was the easy part. The hard part was figuring out how to actually produce content across all of them without losing quality or losing my mind.
My first instinct was to build one master GPT that did everything — write the lyrics, generate the video prompts, produce the SEO metadata. One prompt, one output, done.
It broke immediately.
When you ask a single model to context-switch between writing culturally authentic poetry, describing visual scenes, and formatting search metadata, it gets confused. It starts guessing. The lyrics would be good but the video prompt would drift. Fix the metadata and the lyrics would break. Every time I touched one variable it destabilized something else. And processing one complete asset end-to-end took too long to ever scale.
The solution wasn’t a better prompt. It was splitting the work.
I tore down the master GPT and built three separate specialized ones — The Lyricist, The Creative, and The Metadata. Each one does one job and does it well. The Lyricist doesn’t know anything about video. The Creative doesn’t touch SEO. They don’t interfere with each other.
That separation changed everything. Instead of one asset at a time I could batch ten songs through the Lyricist simultaneously, then feed all ten into the Creative GPT at once. The assembly line was born.
On quality — because scale without quality is just noise.
Producing fast doesn’t mean producing slop. Culturally tone-deaf content gets rejected immediately by real audiences and the algorithm penalizes it fast. So I built a two-layer quality check.
The first layer was a dedicated QA GPT whose only job was to audit Lyricist outputs — flagging forced slang, cultural mismatches, hallucinated references. The second layer was my own review. Because the modular system standardized the outputs, anything off stood out immediately.
When something slipped through — and occasionally it did — the protocol was simple. Pull it down, fix the prompt, re-upload. No exceptions.
The soul of each track isn’t in the generation. It’s in the blueprint. The Master Prompt locks in the cultural corridor — exact instrumentation, regional slang ratios, banned words, specific genre rules. The AI executes variations within those boundaries. The authenticity doesn’t degrade at scale because the constraints never change.
Three hundred assets later, that system held.