Red Teaming is the practice of deliberately trying to make an AI model fail, misbehave, or produce harmful output, so those weaknesses get found and fixed before the model reaches real users. Instead of testing whether a model works correctly under normal conditions, red teaming specifically looks for the edge cases where it breaks.

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Why it matters

A model can pass every standard test and still produce a genuinely harmful or embarrassing response when someone deliberately tries to trick it or push it toward a topic it wasn't trained to handle carefully. Red teaming exists because relying only on normal testing leaves those gaps unknown until an actual user stumbles onto one, often publicly. It's become a standard part of how AI labs release models responsibly, not just a nice to have step.

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How it actually works

A red team typically writes prompts specifically designed to bypass a model's safety guidelines, sometimes called jailbreak attempts, and documents exactly which techniques succeeded and how the model responded. Findings usually get turned into new training examples or additional guardrails so the same failure doesn't happen again once the model reaches real users. Because new attack techniques keep showing up, red teaming isn't a one time step before launch, it's an ongoing process even after a model ships.

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Where it fits into AI safety work

Major AI labs, including OpenAI and Anthropic, publish red teaming results as part of releasing new models, describing what kinds of harmful behavior they specifically tested for and what they found. Some of this work is done internally by a company's own safety team, and some labs also bring in outside experts specifically to try attacks the internal team might not think of.