Definition
AI alignment is the broad research effort to ensure that an AI system's behavior and objectives match human values and intentions. For language models, alignment is what separates a raw next-token predictor from a system that is helpful, truthful, and unlikely to cause harm, and it is an active area of both engineering and open research questions.
Outer and inner alignment
The problem is often split in two. Outer alignment is about specifying the right objective: choosing reward or loss functions that actually capture what we want. Inner alignment asks whether the model, once trained, genuinely pursues that intended objective, especially on inputs unlike anything in its training data. Both can fail independently. A perfectly specified goal can still be mislearned, and a faithfully optimized model can still chase the wrong target.
The HHH framework
A widely used shorthand for alignment goals is "helpful, honest, and harmless" (HHH). A well-aligned assistant should help the user, avoid stating falsehoods, and avoid generating harmful content, while acknowledging that these criteria are subtle and often in tension. Achieving them in practice relies on the post-training techniques that dominate modern model development.
Alignment is the umbrella over concrete methods such as RLHF, DPO, and Constitutional AI. For those pursuing sovereign AI, alignment is a reminder that every model embeds choices about whose values it serves, which is a strong argument for running models you can inspect and tune.
In Simple Terms
AI alignment is the broad research effort to ensure that an AI system’s behavior and objectives match human values and intentions. For language models, alignment…