This is also the best approach I've found thus far when I'm seeing how well LLMs can form narrative content.
I don't frame its prompt as antagonistic though - I've found in the past (with weaker models, so YMMV) that this can be overly officious, sometimes blocking more creative outputs that you'd want to retain.
The structure I've found that works best is to have six or seven agents chained, each roughly mimicking a part of the mind, or a role in film production. Broadly:
- A high-temp "Id" agent, tuned to output only vaguely related noise. This really helps creativity.
- An "Ego" agent, who receives the "Id" noise and is then given the initial response task.
- A low-temp "Super-Ego" or "script supervisor" agent, who can grep back across longer contexts to check detail, and is asked to ensure that the initial response is within narrative reason. Not telling it that one role of the dialogue was "user" and one was "assistant" really helps with it not siding with the user.
- A "continuity editor" agent, who is explicitly tasked with world and character lore-checking, building and updating character & world MD docs, etc.
- A "prose editor" agent, whose sole task is to ensure it's tonally in-line with initial guidelines.
You can add more as needed, depending on what is important to you.
I think expecting competent narrative from a single model is a big ask. When writing and telling or performing a story, you have to engage several different parts of the brain, with very different tasks. The creative part of the brain has to have lots of bad ideas in it to surface a compelling idea; the parts dealing with immersion and/or realism have to incredibly restrained.
The Id agent is very important. By appending 100 tokens of noise to a prompt asking: "Write a short story about [subject]", then asking an LLM to blindly score the short stories generated across a range of creativity metrics (such as they can exist!) I personally saw a ~40% score increase vs control over 3k short stories.