Quick answer: Red-teaming, jailbreak resistance, alignment evaluation.
AI Safety Evaluation is the practice of systematically testing AI systems to identify vulnerabilities, misalignments, and failure modes before deployment. It involves red-teaming (adversarial testing), jailbreak resistance analysis, and alignment evaluation—techniques to ensure AI models behave safely and reliably under unexpected or malicious inputs.
This skill lets you build robust safety frameworks, develop testing methodologies for large language models, create adversarial prompts to expose weaknesses, and establish guardrails that prevent harmful outputs. You might design evaluation benchmarks for chatbots, test whether models maintain alignment under prompt injection attacks, or develop metrics to measure AI system trustworthiness—work that directly impacts whether deployed AI systems can be safely trusted by users and organizations.