In laymen's terms, people can help an AI adjust their methods for achieving a particular result by providing a correct result when an undesirable or wrong answer is presented. This result is then incorporated into the AI's collection of associations or connections, or training weights between objects, through a separate program, allowing for widespread, relative adjustment. Many times, human feedback is framed as 'positive' or 'negative' rewards or consequences through the method of Reinforcement Learning.

In a technical way, AI models learn from human feedback via the method of supervised learning, which works through training the AI on a labeled dataset, which includes a set of samples paired with a particular label or target. As the model learns to predict these labels, it generates a neural network, or weighted graph, that is used to filter the input through multiple layers of nodes into a certain output. During and after this process, human feedback is provided upon the output over various iterations, where the model's weights are tuned to become more accurate until it reaches a certain threshold for accuracy. There are various techniques for updating these weights, an example being backpropagation which entails the usage of gradients to reduce a certain cost function, et cetera.
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