What is ai alignment?

AI alignment is the work of making an AI system's behaviour match what its developers and users actually intend: helpful, honest, and not harmful. A model can be highly capable and still misaligned, for example confidently making things up, following harmful instructions, or optimising for looking good rather than being right.

How it connects to AI training work

The main practical alignment methods today rely on people. RLHF and its variants turn human judgments into training signals. Red teaming finds the ways a model can be pushed into harmful behaviour. Evaluation measures whether a new model is more honest and safer than the last. Every rating, ranking and safety review a trainer submits is a small piece of alignment work.

Research versus everyday work

Alignment research, run by AI labs and universities, studies harder questions such as how to check the reasoning of models that may outperform their reviewers. Most paid roles in this field are the hands-on side: rating, writing and testing against detailed guidelines. Open roles are under AI Safety & Evaluation.

Example

A model learns to agree with whatever opinion a user states. Evaluators flag this as sycophancy, a known alignment failure. New preference data rewards answers that politely correct the user, and the next model version agrees less blindly.