Chapter 1 introduced the challenges inherent in scaling LLM alignment. Now, we concentrate on the theoretical underpinnings of one proposed solution: Constitutional AI (CAI). This chapter explains how CAI aims to guide model behavior according to predefined principles, reducing the reliance on direct human feedback for every generated response.

You will learn about:

• The fundamental concepts driving CAI.
• Approaches and considerations for creating effective constitutions.
• The mechanics of the supervised learning phase, where an AI critiques and revises outputs based on the constitution.
• The mathematical representation of the CAI feedback loop.
• How CAI compares to standard instruction fine-tuning methods.
• Potential weaknesses and critical viewpoints regarding the CAI framework.

By the end of this chapter, you will have a solid conceptual grasp of how CAI functions and the reasoning behind its design.