Chapter 3: Implementing Constitutional AI Systems

Following the theoretical examination of Constitutional AI in the previous chapter, this chapter provides a practical guide to building the components of a CAI pipeline. We move from concepts to code, detailing the steps required to implement the supervised learning phase central to CAI.

You will learn how to:

Structure and prepare the constitution document ( $\mathcal{K}$ ) for automated use.
Generate initial responses from the base Large Language Model ( $M_{base}$ ).
Implement the AI systems responsible for generating critiques ( $C$ ) based on $\mathcal{K}$ .
Implement the AI systems responsible for revising responses based on $C$ .
Construct the supervised fine-tuning (SFT) dataset, pairing critiques with improved responses ( $R$ ).
Fine-tune the LLM ( $M_{SFT}$ ) using the generated CAI dataset.

We will also discuss common implementation challenges, debugging strategies, and include a practical exercise focused on building a core component: the AI critique generation step. This chapter equips you with the practical knowledge to start building CAI systems.

Sections

3.1 Setting up the Constitution Document
3.2 Generating Initial Responses
3.3 Implementing the AI Critiquer Model
3.4 Implementing the AI Revision Model
3.5 Constructing the Supervised Fine-Tuning Dataset
3.6 Fine-Tuning the LLM with CAI Data
3.7 Debugging and Iterating on the CAI Process
3.8 Hands-on Practical: Building a Simple CAI Critique Step