  The task of cultivating healthy communication in online communities becomes
increasingly urgent, as gaming and social media experiences become
progressively more immersive and life-like. We approach the challenge of
moderating online communities by training student models using a large language
model (LLM). We use zero-shot learning models to distill and expand datasets
followed by a few-shot learning and a fine-tuning approach, leveraging
open-access generative pre-trained transformer models (GPT) from OpenAI. Our
preliminary findings suggest, that when properly trained, LLMs can excel in
identifying actor intentions, moderating toxic comments, and rewarding positive
contributions. The student models perform above-expectation in non-contextual
assignments such as identifying classically toxic behavior and perform
sufficiently on contextual assignments such as identifying positive
contributions to online discourse. Further, using open-access models like
OpenAI's GPT we experience a step-change in the development process for what
has historically been a complex modeling task. We contribute to the information
system (IS) discourse with a rapid development framework on the application of
generative AI in content online moderation and management of culture in
decentralized, pseudonymous communities by providing a sample model suite of
industrial-ready generative AI models based on open-access LLMs.
