Why "African" Is Far Too Generic
A note before we begin: This guide explores physical characteristics that AI models may or may not capture accurately. These are generalizations—individuals within any ethnicity vary enormously. The goal isn't to stereotype but to understand AI's technical capabilities and limitations when generating specific African ethnicities.
When you prompt an AI model with "African woman," you're asking it to represent 1.4 billion people across 54 countries with vastly different physical characteristics. A Yoruba woman from Nigeria looks distinctly different from an Ethiopian Amhara, who looks different from a Moroccan Berber, who looks different from a South African Zulu. The result of a generic prompt is typically an averaged face that represents no one authentically.
Professional use cases demand specificity: casting directors need distinct ethnicities, stock photographers need authentic representation, character designers need visual accuracy. This guide demonstrates what's achievable with precise prompting—and where current models still fall short.
We're using Nano Banana Pro for this exploration. Judge the results yourself—where does the model succeed in capturing regional diversity, and where does it fall back on generic defaults?