A/B testing (also called split testing) is a statistical method for comparing two versions of something — a model, a prompt, a UI element, or a business process — to determine which performs better. Users or data points are randomly divided into two groups, each exposed to one version, and the difference in outcomes is measured. In AI, A/B testing might compare two prompt templates, two model versions, or a human-written response against an AI-generated one. It provides empirical evidence for decision-making rather than relying on intuition.