RLHF is a training method that uses people's preferences between model outputs to shape a model's behaviour. It is mainly used to make language models more helpful, honest and safe.
AI governance is the set of policies, roles and controls an organisation uses to decide how AI systems are built, approved, monitored and retired. It is how responsible AI principles get enforced.
Fine-tuning is further training of an existing model on a smaller, task-specific dataset so it adopts a particular style, format or skill. It changes how a model behaves without the cost of training a new one from scratch.
Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions. The behaviour comes from training on examples rather than from rules written by hand.
Data labeling is adding correct answers, such as categories, tags, boxes or ratings, to raw examples. The labelled data is then used to train or evaluate a machine learning model.
Human-in-the-loop is a design approach where people review, correct or approve an AI system's outputs or actions at chosen points. It keeps human judgement in decisions that are risky, ambiguous or hard to reverse.
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