constitutional-ai

Constitutional AI uses a constitution of guiding principles to enable models to self-critique their own outputs and revise them for safety. This approach reduces reliance on human labels by leveraging AI feedback loops in both supervised learning and reinforcement learning phases.

Yes. Constitutional AI integrates with transformers, torch, and the TRL library to implement end-to-end supervised fine-tuning and PPO/RLAIF workflows for safety-aligned language model training.

Self-critique is the supervised learning phase where models revise outputs based on chain-of-thought reasoning against constitutional principles. RLAIF extends this by training reward models on AI feedback to optimize policy through reinforcement learning, creating a two-phase alignment process.

Define a set of principles representing your safety requirements, then use the constitutional AI framework to generate chain-of-thought critiques of model outputs and revised versions. These critiques and revisions form the dataset for fine-tuning.

Constitutional AI is suited for harm reduction in policy, risk assessment, and responsible AI development when human labeling is costly or infeasible. It works best when clear constitutional principles can guide critique, though it complements rather than entirely replaces human oversight in safety-critical domains.

Constitutional AI depends on well-defined principles; ambiguous or incomplete constitutions may produce inconsistent critiques. It also assumes the underlying model has sufficient capability to self-critique meaningfully and may not catch all edge cases without additional human validation in high-stakes scenarios.