A Practical Guide to AI Risk Evals
Today’s automated AI risk evals typically use one of three approaches to catch risk:
- LLM-as-a-judge -- a single model, single prompt, scored pass/fail
- Runtime Safeguards, — lightweight filtering applied as content is generated
- Red Teaming -— simulating adversaries trying to break a model's safety protections
But these methods miss the risks that matter most — and over-report risks that aren't there — while giving too little detail to act on either.
The result? Unnecessary risk or false alarms that slow you down.
In this guide, we break down how to ship faster by flagging the right risks.
