General
AI Trainer
AI trainers (also called RLHF labelers, preference annotators, or AI quality raters) play a central role in aligning AI models. They evaluate model outputs for quality, accuracy, and safety; rank competing responses; write ideal example responses; and red-team models to find failure modes.
As AI products scale, AI trainer roles are expanding across specializations—domain experts (legal, medical, coding) provide high-signal preference data for fine-tuning specialist models. The quality of AI trainers' feedback directly shapes the model behaviors that millions of users experience.
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Related Terms
Techniques & Methods
Reinforcement Learning from Human Feedback (RLHF)
Training technique that refines AI models using feedback from human evaluators on output quality.
Miscellaneous
Training Data
The labeled or unlabeled dataset used to fit a model's parameters during the learning process.
Techniques & Methods
AI Alignment
The research field and engineering practice of building AI systems that reliably pursue goals humans actually want, remain controllable, and avoid harmful side effects — operationalized through RLHF, Constitutional AI, evaluations, and interpretability.
Miscellaneous
Label
Annotation indicating the correct output or category for a training example in supervised learning.