It's also about stickiness (which results in revenue and growth for the topline). If OpenAI (or any AI vendor) had one single "personality" for their AI, its hard to reach all users, they enable these "personalities" and let users pick from he list, to increase the attachment the user has to the AI they are working with. That then reduces churn and (in theory) increases consumption and revenue.

Well, not really.

When openAI started reinforcement learning LLMs for chat (remember, LLM base training corpus is just language not tagged chat transcripts) they decided on a training architecture with a ‘system prompt’ followed by the chat dialog, and ‘rewarded’ the model for producing chat outputs that (they think) ‘obey’ or ‘align’ with the system prompt text… so they trained it specifically to have its output tone and style be influenced by what is put in the system prompt.

Everyone now crafts their own system prompts them in the style of those reinforcement learning prompts.

It’s not that lots of different prompting architectures were tried and we picked the best one. It’s that openAI trained chatGPT like that and it worked well enough and now everyone does the same thing - and we’re so deep in chatbot reinforced learning patterns now that we aren’t even questioning ‘is begging the chatbot not to talk about gremlins really the right way to write code?’

Exactly my thinking. The same reason why capitalizing and putting the word NEVER in asterisks makes the model more obedient. Or repeating twice. For whatever reason, it just works.