A feedback loop in AI is the structured process of capturing user input about a model’s performance and using it to refine and improve that model over time. Effective feedback loops capture both qualitative insights (e.g., comments, suggestions) and quantitative measures (e.g., ratings, usage data). In enterprise deployments, feedback loops allow data science teams to prioritize improvements based on real-world business impact, rather than just technical metrics. AI Squared embeds feedback directly into Data Apps, so feedback is collected at the moment of use, making it highly contextual and actionable.