red teaming

**Red Teaming** for AI is the **structured adversarial evaluation where a team systematically tries to make the model fail, produce harmful outputs, or behave unexpectedly** — proactively discovering vulnerabilities, biases, and failure modes before deployment. **Red Teaming Approaches** - **Manual**: Human red teamers craft inputs designed to expose model weaknesses. - **Automated**: Use other ML models (red team LLMs) to generate adversarial prompts. - **Structured**: Follow a taxonomy of potential failure modes and systematically test each category. - **Domain-Specific**: In semiconductor AI, test with physically implausible inputs, edge-case recipes, and adversarial sensor data. **Why It Matters** - **Pre-Deployment Safety**: Discover dangerous failure modes before the model is in production. - **Security**: Identifies potential adversarial attack vectors that could be exploited. - **Trust**: Demonstrates due diligence in model safety — increasingly required by AI governance frameworks. **Red Teaming** is **the authorized attack team** — systematically trying to break the model to improve it before real users encounter the same failures.