Red Teaming: Definition and Examples

Red teaming, applied to artificial intelligence, refers to a structured process where testers (human or automated) deliberately attempt to cause a language model to fail, bypass, or be manipulated. The goal is to identify weaknesses before they are exploited in real-world conditions: generation of dangerous content, discriminatory biases, leaks of sensitive information, or bypassing guardrails.

This practice is directly inspired by the military and cybersecurity domains, where a "red team" plays the role of the adversary to test an organization's defenses. In the context of AI, red teamers design adversarial prompts, jailbreak scenarios, and edge cases to map the model's undesirable behaviors.

Red teaming has become an essential step in the development cycle of large language models (LLMs). Companies like OpenAI, Anthropic, and Google DeepMind organize red teaming campaigns before every major deployment, calling on experts in security, ethics, and various specialized fields.

In prompt engineering, understanding red teaming not only allows designing more robust systems but also better formulating system prompts and guardrails. A prompt engineer who masters adversarial techniques can anticipate manipulation attempts and reinforce the reliability of their applications.