In the context of large language models, reasoning refers to the ability to draw inferences and connect disparate pieces of information in a logical manner to solve complex problems. This capability involves both recognizing patterns from the training data and employing strategies—such as chain-of-thought prompting—to simulate multi-step problem solving. Reasoning enables models to generate coherent and contextually relevant responses, thereby increasing their utility in tasks that require analytical thinking, such as mathematical problem solving and decision support.