The classifiers approach aims to improve user experience for non-dangerous tasks while effectively blocking harmful content.

The techniques used to bypass classifiers often involve encoding mechanisms that obscure harmful outputs, making them less detectable.

Using a more capable classifier after serving a response can help flag potentially harmful outputs and involve human reviewers for the most dangerous responses.

Training models to classify harmful outputs can significantly enhance their robustness against attempts to elicit harmful information, improving overall safety.

Constitutional classifiers use a natural language set of rules to specify harmful and harmless topics, guiding the model's responses accordingly.

Classifiers are decoupled from the text generation model, allowing for easier updates without unintended consequences in other domains, which gives customers a lot of assurance that there are no major changes happening in general to the model.

Synthetic data is generated from broad categories of user requests, allowing the model to explore specific harmful examples and improve its classifiers.

Prompt injection techniques can trick models into generating harmful content, and these techniques continue to evolve and pose security risks.

Regulatory measures could backfire if they lead labs to train AI to avoid revealing harmful intentions.