You should not ever expect an LLM to actually do what you want without handholding, and randomness in particular is one of the places it fails badly. This is probably fundamental.

That said, this is also not helped by the fact that all of the default interfaces lack many essential features, so you have to build the interface yourself. Neither "clear the context on every attempt" nor "reuse the context repeatedly" will give good results, but having one context producing just one-line summaries, then fresh contexts expanding each one will do slightly less badly.

(If you actually want the LLM to do something useful, there are many more things that need to be added beyond this)

Someone mentioned generating millions of (very short) stories with an LLM a few weeks ago: https://news.ycombinator.com/item?id=42577644

They linked to an interactive explorer that nicely shows the diversity of the dataset, and the HF repo links to the GitHub repo that has the code that generated the stories: https://github.com/lennart-finke/simple_stories_generate

So, it seems there are ways to get varied stories.

> Do you find that it actually generates varied and diverse stories? Or does it just fall into the same 3 grooves?

> Last week I tried to get an LLM (one of the recent Llama models running through Groq, it was 70B I believe) to produce randomly generated prompts in a variety of styles and it kept producing cyberpunk scifi stuff.

100% relevant: "Someday" <https://en.wikipedia.org/wiki/Someday_(short_story)> by Isaac Asimov, 1956

Generate a list of 5000 possible topics you’d like it to talk about. Randomly pick one and inject that into your prompt.

It's a 3b model so the creativity is pretty limited. What helped for me was prompting for specific stories in specific styles. I have a python script that randomizes the prompt and the writing style, including asking for specific author styles.

Set temperature to 1.0