Example Concept: Safety tuning verification involves conducting controlled tests where the AI model is exposed to scenarios that previously led to high-risk outputs. By comparing the model's responses before and after tuning, you can assess whether the safety mechanisms effectively mitigate risks. This process often includes using safety evaluation metrics, such as false positive rates for harmful outputs, and ensuring compliance with established safety frameworks like the NIST AI Risk Management Framework.