I'm not sure what exactly you mean by censorship here because nothing that I know of that they do is actually censorship. In particular they don't do the sort o

I'm not sure what exactly you mean by censorship here because nothing that I know of that they do is actually censorship. In particular they don't do the sort of prompt stuffing you're talking about, because that would just use up the context window of the LLM and you'd know because the context window is public and as a dev you can use the whole window. There is a "system" role that allows application developers to "whisper in the ear" of the LLM and provide prompts into the context that are different from the user prompts. That's how you build chat apps where you have a user providing some instruction but you have some overall rails around the conversation. That being said, the question of how to account for and deal with bias in llms is an active area of research, and (like everything else in llms) comes down to vector math. See https://arxiv.org/pdf/2106.13219.pdf https://arxiv.org/pdf/2106.13219.pdf for example. Content moderation in openAI is the subject of this paper https://arxiv.org/abs/2208.03274 https://arxiv.org/abs/2208.03274 and they have published their api here https://openai.com/blog/new-and-improved-content-moderation-tooling https://openai.com/blog/new-and-improved-content-moderation-... That also includes their reference dataset for training.