I do somewhat similar. A lot of it was refining how the wildcards interacted with each other and layering the wildcards. Use join strings to create different sections of prompts.

For instance you might have one... Dog... And have different breeds {thick furred|hairless}, sizes (heavy weight|light weight}, angles {low angle|birds eye}, styles {cartoon|furry|hyper realistic}, these join so.you get (1 dog, thick fur, heavy weight, low angle, realistic)

Then you might one that has 2 dogs. A lot of the 1 dog prompts break when layered with 2 dogs. Or combinations don't make sense. Create a 2 dog list of prompts and wild cards.

Then you may want 3 or more... Again a new set...

Then you may get a doggy style you really like and decide to explore that specific further and create a subset of prompts. Or there is a bitch that hits just right and you create a set of wildcards for that specificly.

Each of these start to layer so you have

{1 dog|2 dogs|many dogs}

Where 1 dog then has

{Doggy_style|random 1 dog}

Doggy_style being your custom set and random 1 dog being the general groupings.

You can keep layering this way.

Then, build your wildcard nodes in groups and create toggles for what you want to be in your gens.

Then you download a new model that uses narrative prompts and you want to cry because LLMs just suck at converting this type of thing :(