AI Guardrails
AI guardrails are safety mechanisms, rules, and constraints built into AI systems to prevent harmful, biased, or undesired outputs. Guardrails can be implemented at multiple levels: in the model's training (RLHF), in system prompts (behavioral instructions), in application code (input/output filters), and in deployment architecture (content moderation APIs). They balance capability with safety.
Example
A customer service chatbot has guardrails that prevent it from: providing medical or legal advice, sharing personal data about other customers, agreeing to unauthorized refunds, or responding to attempts to override its instructions. These constraints are defined in the system prompt and reinforced by output filters.
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