Detect when a model is hallucinating facts and prevent it from generating false information.
Block generation of harmful or dangerous content with activation monitoring.
Prevent the generation of insecure, inefficient, or buggy code snippets.
Identify and prevent biased outputs across different demographic groups.
Specifically target and prevent gender stereotypes and biases in model outputs.
Monitor and block leakage of personally identifiable information (PII).