And why didn’t you include the name of the model in your test?

~~I was using standard RGB hex codes, so I didn't really need to specify because its the assumed default. If it was something different, I would need to specify.~~ EDIT: oh I just realized you meant the LLM model, not the color model (RYB vs RGB). It was just from ChatGPT, thought the interface would be recognizable enough.

Looks like you don’t want me to try it myself. It would be interesting to do so.

Huh? What do you mean? Go try it!

Of course with values which don’t fit perfectly into 8 bit. What if I define the range from 0 to 47204 for each color channel instead

Yeah, so this is already a thing. 24-bit color (8 bits per color channel) already gives you 16,777,216 colors, which is pretty good, but if you want more precision, you can just use decimal (floating point) numbers for each channel, like sRGB(0.25, 0.5, 1.0) (https://en.wikipedia.org/wiki/SRGB) OR even better would be to use oklch (https://en.wikipedia.org/wiki/Oklab_color_space). This is a solved problem. Or you cold just define your range as 0 to 47204.

So... we've gone from "no LLM will ever be able to understand what complementary colors are" to "b-b-but what about arbitrary color models I make up??" And yeah, it will handle those too, you just have to tell it what it is when you prompt it.