Feedback Loop

A feedback loop in LLMOps is the process of collecting signals about model output quality — thumbs ratings, correction edits, downstream task success, or expert annotations — and feeding those signals back into prompt improvement, fine-tuning datasets, or evaluation test suites. Closing the feedback loop is what distinguishes a live production system that improves over time from one that stagnates at its initial deployment quality. Feedback data must be carefully curated to avoid amplifying biases present in early user interactions.