Training models using Constitutional AI (CAI) and Reinforcement Learning from AI Feedback (RLAIF) is only part of the process. Verifying that these techniques have successfully instilled the desired alignment properties requires specialized evaluation methods. Standard NLP benchmarks often fall short in assessing safety, helpfulness according to specific principles, or resistance to subtle manipulation.

This chapter focuses on the methodologies needed to rigorously assess models aligned through CAI and RLAIF. You will learn about:

• Developing and applying metrics tailored specifically to alignment goals, moving past generic performance measures.
• Employing systematic red teaming strategies to actively search for model vulnerabilities and failure modes.
• Conducting robustness tests using adversarial or out-of-distribution inputs to gauge model resilience.
• Analyzing failure patterns characteristic of AI feedback mechanisms, such as sycophancy or reward model exploitation.
• Applying appropriate statistical techniques to ensure evaluation results are meaningful.
• Performing qualitative analysis to gain a deeper understanding of model behavior and alignment characteristics.

Mastering these evaluation techniques is essential for building confidence in the safety and reliability of aligned LLMs and for iterating effectively on the alignment process itself. We will also cover a practical exercise in designing a red teaming test suite.