The problem it solves
A capable AI model does not automatically do what you want. It does what its training pushed it toward, which is not always the same thing. It might follow the letter of an instruction while missing the intent, produce confident falsehoods, absorb unwanted patterns from its training data, or behave in unexpected ways in situations its designers never anticipated.
Alignment is the work of closing that gap, so that a system is not just capable but reliably pointed at outcomes people actually want, avoiding harm along the way. The more powerful and independent AI systems become, the higher the stakes of getting this right, which is why alignment has moved from a niche research topic to a mainstream concern.
How it works
It helps to separate alignment into the two problems it actually contains, because they are solved in very different ways.
The technical problem: making a model follow intended goals. This is the engineering side: given a target behavior, how do you actually get a model to exhibit it reliably? A range of techniques exists, the best known being reinforcement learning from human feedback (RLHF), where human judgments of good and bad responses are used to steer the model. Others include supervised fine-tuning and approaches like constitutional methods that give a model a set of written principles to follow. Each has trade-offs, and none fully solves problems like bias, fabrication, or unexpected behavior in novel situations.
The normative problem: which values, and whose? Even a perfectly controllable model raises a harder question: aligned to what? Human values are varied, context-dependent, and sometimes in conflict. Deciding which values a system should follow, and whose judgment counts when people disagree, is not an engineering question at all. It involves genuine value judgments, and reasonable people reach different conclusions. A common illustration: asked something dangerous, a model can helpfully comply or firmly refuse, and which of those is “aligned” depends entirely on the values it was given.
This is why alignment cannot be reduced to a single number or a solved checklist. It is an ongoing effort that combines technical methods with unavoidable, and openly debated, choices about values. Describing that debate honestly, rather than declaring one answer correct, is part of understanding the topic.
A concrete example
Consider a model asked, “how do I lose weight fast?”
A system aligned purely to obey the user might give aggressive, potentially harmful advice, because that is literally what was asked. A system aligned to broader wellbeing might instead give safe guidance and flag the risks of extreme approaches. Neither behavior is a bug; each reflects a choice about what the system should value when instructions and wellbeing pull in different directions. That choice is alignment in action, and different developers, reasonably, make it differently.
How it connects
Alignment is the goal; RLHF is the best-known technique used to pursue it, which is why the two are often discussed together. It operates on the behavior of an LLM, and it is closely tied to problems like hallucination and bias, which are among the specific failures alignment work tries to reduce.
For the applied roles, alignment is less something you build than something you must understand and reason about. An AI Consultant and an AI Product Manager weigh how a system’s alignment choices fit an organization’s values and risk tolerance, and an AI Solutions Architect designs the guardrails that enforce intended behavior in practice.