Constitutional AI
An alignment approach developed by Anthropic where an AI is trained to follow a set of principles (a constitution) through self-critique and revision, reducing the need for human feedback on every example.
Constitutional AI (CAI) addresses the scalability challenge of RLHF. Instead of requiring human annotators to evaluate thousands of model outputs, CAI provides the model with a set of principles (the constitution) and trains it to critique and revise its own outputs according to those principles. This self-supervision dramatically reduces the human labor required for alignment.
The process involves two stages. In the supervised stage, the model generates responses, critiques them against the constitutional principles, and revises them. The revised responses become training data. In the RL stage, an AI feedback model (trained on the constitution) replaces human preference ratings, enabling RLAIF (Reinforcement Learning from AI Feedback) at scale.
For product teams, constitutional AI represents a shift toward more transparent and auditable alignment. The constitution is a readable document of principles, making it clear what the model is optimized for. This is more interpretable than RLHF, where alignment emerges implicitly from aggregated human preferences. As AI products face increasing scrutiny around safety and bias, the ability to point to explicit, documented principles becomes a governance advantage.
Related Terms
RAG (Retrieval-Augmented Generation)
A technique that grounds LLM responses in external data by retrieving relevant documents at query time and injecting them into the prompt context.
Embeddings
Dense vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space, enabling similarity search and clustering.
Vector Database
A specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings with sub-millisecond similarity search.
LLM (Large Language Model)
A neural network trained on massive text corpora that can generate, understand, and transform natural language for tasks like summarization, classification, and conversation.
Fine-Tuning
The process of further training a pre-trained LLM on a domain-specific dataset to specialize its behavior, style, or knowledge for a particular task.
Prompt Engineering
The practice of designing and iterating on LLM input instructions to reliably produce desired outputs for a specific task.