Guardrails and filters serve similar purposes but differ in their scope and implementation. Filters are simpler mechanisms that block or restrict specific content based on predefined rules or keywords, such as preventing explicit or offensive language.
Guardrails, on the other hand, are broader and more sophisticated. They include strategies like fine-tuning, reinforcement learning with human feedback (RLHF), and dynamic monitoring to guide the model's overall behavior. Guardrails aim to ensure the model produces coherent, ethical, and contextually appropriate outputs across a range of situations.
While filters operate as reactive tools for specific issues, guardrails provide proactive and comprehensive solutions to align the model’s behavior with organizational values and user expectations. Both are often used together for effective model governance.