Constitutional AI
A training approach where AI models are given a set of principles (a 'constitution') and learn to self-critique and revise their outputs to comply with those principles. Reduces reliance on human feedback for safety alignment.
Why it matters
Teaching AI to behave well usually means paying people to rate thousands of responses, which is slow and hard to scale. Constitutional AI gives the model a written set of principles and lets it critique and fix its own answers against them. This makes safety training more consistent and transparent, since the guiding rules are actually written down rather than buried in scattered human judgments.
In practice
A model drafts a reply that's technically correct but rude. Under Constitutional AI, it checks its draft against a principle like "be helpful and respectful," notices the tone problem, and rewrites the answer to be polite while keeping the useful content. This self-review happens during training, so by the time you use the model, it has already learned to lean toward responses that follow those principles.
Related terms
AI Alignment
The research challenge of ensuring AI systems pursue goals that are beneficial to humans. Misaligned AI could technically achieve its objective while causing unintended harm. Alignment research aims to make AI reliably helpful, harmless, and honest.
Bias (in AI)
Systematic errors in AI outputs reflecting prejudices in training data. Can manifest as gender stereotyping, racial assumptions, or cultural insensitivity in generated content.
Data Poisoning
A security attack where malicious data is deliberately introduced into AI training sets to manipulate model behavior. Can cause models to produce biased outputs, bypass safety filters, or leak sensitive information.
GDPR
The European Union's General Data Protection Regulation governing how personal data is collected, stored, and processed. Important when choosing AI tools that handle your data.
Guardrails
Safety mechanisms built into AI systems to prevent harmful, biased, or off-topic outputs. Includes content filters, topic restrictions, output validation, and behavioral boundaries that keep AI responses within acceptable limits.
Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated. Common with statistics, citations, and historical claims.