Definition
A technique where organizations test AI models against real-world usage patterns (drawn from production logs) before release, predicting how the model will behave in the wild. Simulation bridges the gap between lab safety testing and real-world deployment.
Why it matters
Safety evaluations conducted in lab settings often diverge from real-world behavior; simulation reduces the risk of unexpected model failures, jailbreaks, or harmful outputs reaching customers.