In the context of AI, "feedback loops" refer to the cyclical process where the output of a system is fed back into the system as input. This mechanism enables AI models to learn from their predictions and improve over time. Feedback loops facilitate continuous learning by adjusting algorithms based on actual outcomes versus predicted results. This iterative learning process is vital for refining machine learning models, enhancing accuracy, and adapting to changing data patterns. Ultimately, feedback loops drive performance improvements in AI applications.