In some systems, yes, users can configure their own guardrails for LLM interactions, particularly in settings where personalization is key. For example, developers may provide an interface or API that allows users to set preferences for content filtering, tone, and response behavior. This customization can be particularly useful in applications where the audience varies, such as customer service bots, educational tools, or content moderation systems.
However, user-configured guardrails are typically limited to certain aspects, such as filtering explicit content or adjusting the verbosity of responses. While users may be able to adjust these preferences, certain core ethical and safety guidelines (such as preventing harmful content) will remain enforced by the system’s overarching guardrails to ensure compliance with broader legal and ethical standards.
Balancing user customization with necessary safety protocols can be challenging, as overly relaxed guardrails could result in harmful content being generated. For this reason, most systems offer a balance where users can personalize certain features while still adhering to essential safety and ethical boundaries.