self-critiquing

**Self-Critiquing** is an **AI safety technique where the model evaluates and critiques its own outputs** — generating initial responses, then assessing them for errors, harmfulness, bias, or quality issues, and optionally revising bad outputs, serving as an internal quality control mechanism. **Self-Critiquing Methods** - **Generate-Critique-Revise**: Model generates, critiques, then revises — iterative self-improvement. - **Constitutional**: Critique against explicit principles — systematic evaluation framework. - **Chain-of-Thought**: Model reasons about potential issues before giving final output. - **Multi-Aspect**: Critique along multiple dimensions (accuracy, safety, helpfulness, bias). **Why It Matters** - **Safety**: Models can catch their own harmful or incorrect outputs before presenting them. - **Training Signal**: Self-critiques provide training signal for RLAIF — the model generates its own preference data. - **Scalable**: No human oversight needed for every output — the model monitors itself. **Self-Critiquing** is **the AI's inner editor** — evaluating and revising its own outputs against quality and safety standards.