AI alignment
The research and engineering practice of ensuring AI systems pursue the goals their designers intend — covering training-time techniques like RLHF and constitutional AI as well as deployment-time guardrails.
AI alignment is the question: how do you make sure the model does what you want, not what you literally asked? Frontier models are trained on enormous web corpora and emerge with capabilities their creators did not specifically intend. Alignment is the work that brings model behavior in line with human values and the specific deployment's goals.
The training-time stack: instruction tuning teaches the model to follow commands; RLHF (Reinforcement Learning from Human Feedback) teaches it which responses humans prefer; constitutional AI uses a model-graded set of principles to refine behavior. Deployment-time adds system prompts, output filters, and tool-call gates.
For agent operators, "alignment" is less about existential risk and more about practical reliability. Will the agent follow my system prompt? Will it refuse the bad request? Will it use the right tool? Each of those is an alignment problem in miniature, and well-aligned models make agents safer to deploy.
Frequently asked
Is AI alignment the same as AI safety?+
Closely related but not identical. Alignment is specifically about getting the model to pursue intended goals. Safety covers alignment plus everything else — robustness, misuse prevention, fail-safe behavior under unexpected inputs.
Why does alignment matter for agents specifically?+
Agents take actions, not just generate text. A misaligned chatbot says something wrong; a misaligned agent sends the wrong email, deploys the wrong code, refunds the wrong charge. Alignment quality compounds with capability.