a type of machine learning where an AI model (such as a generative AI chatbot) learns via trial-and-error to make better decisions to achieve a goal like 'sounding human.' The model receives rewards or penalties for the actions it takes—such as thumbs up or thumbs down from a user—and learns to maximize the total reward over time.
"We're using reinforcement learning to training our chatbot to provide the right amount of technical information in answers based on the type of user interacting with it."
