How do you design an LLM evaluation feedback loop in production?

A feedback loop collects user feedback, model metrics, and evaluation scores to continuously improve prompts, retrieval, and models.

Production LLM systems continuously improve via feedback loops. These loops collect explicit feedback (thumbs up/down), implicit signals (click-through rates), and automated evaluation scores. This data is used to refine prompts, improve retrieval ranking, or retrain models. The loop closes when updated artifacts are redeployed.