Guardrail selection has typically meant choosing between a purpose-built classifier and an LLM acting as a judge. Decision models such as TypeSafe AI’s Jev have added a third option, promising the flexibility of zero-shot policies without the cost of open-ended generation. TypeSafe launched Jev in mid-September with performance claims based on evaluations it designed and ran itself. Red Hat’s AI Safety team has now put all three approaches through the same benchmarks . It tested nine guardrail configurations on prompt injection and content safety, running each through NVIDIA’s open source...

Source: The New Stack