Red teaming is structured adversarial testing: a team (human, automated, or both) deliberately tries to make your AI system produce unsafe, off-brand, or wrong output. It's the difference between hoping your model behaves and knowing what it'll do under pressure.
Useful red-team categories: prompt injection, jailbreaks, hallucination on edge cases, bias, PII leakage, malicious tool use, and competitor mention. Run them as part of your eval pipeline so a "safety regression" surfaces before users hit it.
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