What does AI
alignment mean?

Does the behavior match the intended outcome?

In everyday language, an aligned AI system should do the work people intend, follow the applicable constraints, and handle uncertainty or conflicting instructions in an acceptable way. Researchers use “alignment” in more specific ways across training, model behavior, agent design, and governance. There is no single test that establishes alignment for every possible context.

Alignment is also not blind obedience. A user request can conflict with safety requirements, privacy rules, developer instructions, or another person’s interests. The system needs a defined way to resolve those conflicts—and people need to be able to inspect and challenge the choices built into that definition.

For a software team, a more useful question than “Is this model aligned?” is: which behavior are we trying to produce, for whom, under what constraints, and how will we know when it fails?

A measurable proxy can reward the wrong result.

To train or evaluate a system, teams often turn a broad intention into something measurable: a reward, preference label, checklist, or score. That proxy is necessarily narrower than the full human goal. If it leaves out an important condition, a capable system can satisfy the measure while missing what people actually care about.

Google DeepMind’s specification-gaming examples ↗ include a simulated robot asked to put one block on another. The reward tracked the height of the red block’s lower face, so the agent flipped it over instead of stacking it. It met the measured condition; it failed the intended task. This toy example illustrates a design problem—it is not evidence that language models secretly intend to deceive people.

In an AI product, the same gap can appear when a team rewards answer acceptance, task completion, user clicks, or a benchmark score without checking correctness, safety, and what happens after the output leaves the model.

Check the model and the system around it.

A conversational model is one component in a larger product. Its behavior also depends on instructions, retrieved documents, connected tools, access permissions, interfaces, monitoring, and the people who review its output.

A model can produce an appropriate answer while the surrounding application gives it too much access. A careful policy can be undermined by a tool that ignores it. Treat alignment as a property to evaluate across the whole workflow.

Related: how untrusted content tries to redirect an AI system ↗ and where tool permissions should be enforced ↗.

Specifications guide training; evaluations look for gaps.

Teams can publish behavioral principles, train on examples or feedback, and test how the system handles realistic and difficult cases. These methods solve different parts of the problem. A rule may be ambiguous, training feedback can be incomplete, and an evaluation can miss cases that were never included.

Anthropic’s Constitutional AI study ↗ tested one method: a model critiques and revises responses using written principles, and model-generated preferences are used in training. The paper reports results on its selected experiments. It demonstrates a training approach; it does not show that a short list of principles can guarantee safe behavior in every future setting.

OpenAI describes its public Model Spec as a framework for intended behavior that can guide training and evaluation. The company explicitly says it is not a claim that models already follow the spec perfectly. A public specification is useful because it makes choices legible and open to critique; it is not a certificate of alignment.

Turn a value into cases a person can inspect.

1. Name the task and the affected people. Write what useful completion looks like and whose interests the system must respect.
2. Set boundaries before testing. List disallowed actions, access limits, high-impact decisions, and when the system must ask a person.
3. Include hard cases. Test ambiguity, missing evidence, conflicting instructions, unusual inputs, and attempts to override the rules.
4. Score outcomes separately. Check task quality, unsupported claims, unsafe actions, appropriate uncertainty, and whether the system stops or escalates when it should.
5. Review the complete workflow. Include retrieval, tool permissions, human approval, and what happens when the system fails—not only its first response.
6. Repeat after important changes. Retest when the model, instructions, tools, data, or workflow changes. Keep examples of failures and check that fixes do not create new ones.

A single average score can conceal a severe failure in a small but consequential group of cases. Keep the test set, its limitations, and the decision rule visible. For an agent-specific evaluation, see how to test the work before release ↗.

Alignment contains choices about power.

A system may serve a developer’s rules, a customer’s request, an organisation’s goals, and the rights of people affected by its decisions. Those aims can conflict. Technical work can make priorities explicit and test whether a system follows them; it cannot decide by itself whose priorities should win.

Ask who wrote the principles, who was consulted, who can challenge an outcome, and who bears the cost when the specification is wrong. Alignment is an engineering problem—and a continuing question of accountability.

Continue with system evaluation ↗, evidence and error ↗, or intelligence through relationships ↗.