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Foundation Lab

Constitutional AI: Principles-Based Alignment

How Claude is trained—principles replace pure human preference labeling

Concept 69 of 100Scaling & AlignmentPhase 7
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#69ConstitutionalScaling & Alignment
key equationr_{revised} = \text{Critique}(r_{initial}, \text{principle})

Selected Foundation Object

Keep the equation fixed; move through the evidence.

Concept 69 of 100ConstitutionalScaling & Alignment / Phase 7: Alignment & RLHF
Current question

The "constitution" is just a set of principles like "be honest" and "don't help with harm"

r_{revised} = \text{Critique}(r_{initial}, \text{principle})
PredictionCommit before the demo.

Ask what should change when the equation is manipulated, then let the visualization test that expectation.

EvidenceCompare local witness and source.

Use the runnable panel, the key equation, and canonical papers as separate forms of evidence for the same object.

InvariantName what survives notation changes.

The useful learning product is the reusable mechanism you can carry into another model, paper, or engineering tradeoff.

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Why It Matters for Modern Models

  • How Claude is trained—principles replace pure human preference labeling
  • Scales better than RLHF: AI can critique faster than humans can label
  • More interpretable: you can see which principles guide behavior

What Tutorials Skip

What is still poorly explained in textbooks and papers:

  • The "constitution" is just a set of principles like "be honest" and "don't help with harm"
  • AI feedback can bootstrap from a smaller set of human preferences
  • Self-critique is iterative: multiple rounds of revision improve outputs

Interactive Visualization

Core Math (Optional Deep Dive)

If you want intuition first, start with the key equation and the visualization. Come back here for the full walkthrough.

Key Equation
rrevised=Critique(rinitial,principle)r_{revised} = \text{Critique}(r_{initial}, \text{principle})

Constitutional AI (CAI) uses a two-stage process:

Stage 1 - Self-Critique: Model generates response, then critiques it:

rcritique=LLM(prompt,rinitial,principle)r_{critique} = \text{LLM}(\text{prompt}, r_{initial}, \text{principle})
rrevised=LLM(prompt,rinitial,rcritique)r_{revised} = \text{LLM}(\text{prompt}, r_{initial}, r_{critique})

Stage 2 - RLAIF: Train reward model on AI-generated preferences:

LRM=−log⁡σ(rθ(rchosen)−rθ(rrejected))\mathcal{L}_{RM} = -\log \sigma(r_\theta(r_{chosen}) - r_\theta(r_{rejected}))

Key insight: Principles ("Be helpful", "Don't be harmful") can guide AI feedback.

Canonical Papers

Constitutional AI: Harmlessness from AI Feedback

Bai et al.2022arXiv
Read paper →

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Bai et al.2022arXiv
Read paper →

Connections

Prerequisites

⚖RLHF🎯DPO

Enables

🤖RLAIF🎯Deliberative🔴Auto RedTeam😴Sleepers

Next Moves

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