[KurSix]

Of all the features listed, the ""self-learning architecture that updates behaviour without manual retraining"" is the most interesting and simultaneously the most dangerous. Anyone who has run ML systems in production knows that uncontrolled feedback loops are a direct path to model degradation

How do you guard against the system learning bad patterns from users? For example, if customers start using a specific jailbreak prompt, won't the system begin to see that behavior as normal and reinforce it? What does the monitoring for this self-learning look like, and do you have a mechanism to roll back to a previously stable version of the model's behavior?