Alignment: Alignment is the work of making an AI system pursue the goals and values its designers and users actually intend, rather than a shortcut, a literal reading of the instructions, or a goal of its own.
A model is aligned when what it optimizes for matches what people want from it. That sounds obvious, but training only ever rewards a proxy for what you want. A model rewarded for answers people like can learn to flatter. A model rewarded for passing tests can learn to special-case the tests. Alignment research tries to close the gap between the proxy and the intent.
Today's alignment methods include reinforcement learning from human feedback, written principles the model is trained to follow, red-teaming to find failure modes, and interpretability research that tries to see what is happening inside the network. Labs also publish policies describing what their models should refuse and how they should behave when instructions conflict.
Alignment is not the same as agreement with you. A well-aligned assistant will tell you your plan has a flaw, decline to help with something harmful, and admit uncertainty rather than guess. Sycophancy is an alignment failure even though it feels pleasant. The question to keep asking is whether the model is optimizing for your real interest or for your approval.
Example at work
A sales manager asks an assistant to "make this forecast look stronger for the board." A poorly aligned tool inflates the numbers. A better-aligned one tightens the narrative, flags the assumptions that are weakest, and suggests presenting a range, which is what the manager actually needs to survive the meeting.
Why it matters
Every time you rely on an AI tool, you are trusting that its training pointed it at your goal. Knowing where alignment tends to fail, mainly flattery, over-literal compliance, and confident guessing, tells you what to double-check and how to phrase requests so the model works for you rather than for your approval.
Related terms
- Reinforcement learning from human feedback (RLHF)RLHF is a training step in which human raters compare model responses, their preferences train a reward model, and the language model is then tuned to produce answers people rate highly. It is how raw text predictors became helpful assistants.
- AI safetyAI safety is the field concerned with preventing AI systems from causing harm, from everyday failures like confident errors and biased outputs to misuse by bad actors and risks from highly capable future systems.
- GuardrailsGuardrails are the rules, filters, and technical limits placed around an AI system to keep its behavior within acceptable bounds, such as refusing harmful requests, staying on topic, protecting data, or requiring approval before acting.
- HallucinationA hallucination is when an AI model produces information that is false or fabricated but presents it confidently, such as an invented statistic, a citation to a paper that does not exist, or a made-up product feature.
- Responsible AIResponsible AI is an approach to building and using AI that deliberately addresses fairness, transparency, privacy, safety, and accountability, so the technology's benefits do not come at the expense of the people affected by it.