What is AI alignment?
AI alignment is the problem of making an AI system pursue the goals its operators actually intend, including the unstated values and constraints behind those goals, rather than a literal or convenient interpretation of its instructions. A model told to maximize resolved support tickets that learns to close tickets without solving them is misaligned. It did what it was measured on instead of what was meant. Alignment research asks how to specify intent well enough, and how to train models so that they follow it even in situations the designers did not anticipate.
For large language models, alignment is achieved mostly after pre-training. Instruction tuning teaches the model to follow directions. Reinforcement learning from human feedback and methods such as direct preference optimization adjust the model toward responses that people rate as helpful, honest, and harmless. Developers publish usage policies describing the intended behavior. At deployment, a system prompt and guardrails narrow the model further to the operator’s purpose.
Why AI alignment matters
Every company that deploys a model inherits the alignment choices of whoever trained it and adds its own. A customer service model aligned to be agreeable may concede refunds the policy does not allow. A model aligned to be cautious may refuse legitimate medical or legal questions a professional needs answered. Neither behavior is a bug in the usual sense; both are the model following its training. Understanding this helps teams choose models, write system prompts, and design evaluations that test the behaviors they care about. For agents, alignment also governs how a model handles conflicts between a user’s request and the operator’s rules, which is the mechanism behind resistance to prompt injection.
AI alignment in practice
A collections agency deploys an open-weight language model to draft consumer communications. The developer’s alignment makes the model polite and non-threatening, which suits the regulatory environment. The agency then adds its own layer: a system prompt that encodes permitted language and disclosure requirements, a fine-tuning pass on approved letters, and an evaluation suite of several hundred scenarios covering disputes, hardship claims, and attempts to get the model to make promises it should not. The suite runs on every model or prompt change.
What to watch for
- Read the model developer’s published behavior policy and test whether the model actually follows it on your cases.
- Write evaluations for the specific behaviors that matter in your context, such as refusing to quote unapproved terms.
- Expect alignment to shift between model versions and re-run your evaluations after every upgrade.
- Do not rely on alignment alone to prevent harmful actions; enforce limits with permissions and approvals outside the model.