Red Teaming
Red teaming is the practice of deliberately trying to make an AI system fail — produce harmful, biased, incorrect, or policy-violating output — using adversarial prompts and creative attack strategies, in order to find and fix weaknesses before real users (or real bad actors) find them.
Red teaming differs from routine QA testing in mindset as much as method: instead of checking "does the system work correctly," it asks "how would someone who wants this to fail actually break it" — jailbreaks, prompt injection, edge-case inputs designed to trigger unsafe outputs, or requests that exploit gaps in the system's stated policies.
Effective red teaming usually mixes structured approaches (a checklist of known attack categories: role-play jailbreaks, encoding tricks, multi-turn manipulation) with open-ended creative probing, since attackers in the wild aren't limited to a checklist either — and results are most useful when every successful attack becomes a permanent regression test, not a one-time finding.
Because model behavior shifts with every update, red teaming isn't a one-time pre-launch exercise — a prompt that reliably failed to jailbreak a model in one version can start succeeding after a seemingly unrelated model or prompt change, which is why mature teams re-run their red-team suite on every release.
