Actually the way you get it to do better is to put more of the burden on interpreting the context on the LLM instead of heavy handed instructions - because the LLMs do understand the context.

For example, here’s Gemini answering what the physical characteristics of 1940s soldiers in Germany might have looked like:

During the Nazi regime in 1940s Germany, racial ideology strictly dictated who was deemed “suitable” for military service. The Wehrmacht, the unified armed forces, prioritized individuals deemed “pure Aryans” based on Nazi racial criteria. These criteria favored individuals with blond hair, blue eyes, and “Nordic” features.

However, it’s important to remember that the reality was more nuanced. As the war progressed and manpower needs intensified, the Nazis relaxed their racial restrictions to some extent, including conscripting individuals with mixed ancestry or physical “imperfections.” Additionally, some minority groups like the Volksdeutsche, Germans living in Eastern Europe, were also incorporated.

I think it could have managed to contextualize the prompt correctly if given the leeway in the instructions. Instead, what’s happened is the instructions given to it ask it to behind the scenes modify the prompt in broad application to randomly include diversity modifiers to what is asked for. So “image of 1940s German soldier” is being modified to “image of black woman 1940s German soldier” for one generation and “image of Asian man 1940s German soldier” for another, which leads to less than ideal results. It should instead be encouraged to modify for diversity and representation but adapted to the context of the request.