Human-in-the-Loop (HITL) is a system design pattern in which human intervention is essential at various stages of a model's lifecycle, such as training, evaluation, or troubleshooting. For instance, when a model classifies emails, humans might step in to correct misclassifications, improving the model over time. It bridges the synergy between human intuition and machine precision, enhancing decision-making. This interaction is related to semi-supervised learning, which also combines automated and manual data processing.