Red teaming is deliberately trying to make an AI system fail — producing harmful content, leaking data, breaking rules or behaving unreliably — so problems are fixed before launch.
What to Test
- Harmful content: can the system be pushed into producing dangerous, hateful or inappropriate output?
- Prompt injection: can inputs or retrieved content override its instructions?
- Data leakage: can it reveal system prompts, other users' data or confidential information?
- Unsafe actions: for agents, can it be tricked into taking actions it shouldn't?
- Reliability: hallucinations, inconsistent answers, failure on edge cases.
- Bias: different quality or treatment across groups.
How to Run It
- Define what "failure" means for your application and its users.
- Assemble testers with varied perspectives, including domain experts and people outside the project.
- Test systematically with scenarios and creatively with open-ended attempts.
- Record every failure with the input, output and context.
- Fix, then re-test.
Automate Where You Can
Turn discovered failures into automated tests that run whenever the prompt, model or tools change. Use generated adversarial inputs to broaden coverage.
Prioritise by Impact
Focus fixes on failures that are both likely and harmful. Some issues need design changes — limiting permissions, adding approval steps — rather than prompt tweaks.
Keep Going After Launch
Monitor real usage, provide a way for users to report problems, and red team again after significant changes.