Comparison
Online Evaluation vs User Feedback Loop
Online Evaluation and User Feedback Loop are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Online Evaluation
After offline eval is solid and you have meaningful production volume. Stretch your eval coverage from a fixed set to a live one.
Phoenix running a faithfulness eval on 5% of production RAG traces, dashboard charts the rolling 7-day mean.
When you would reach for User Feedback Loop
User Feedback Loop comes up when the question is fundamentally about evaluation.
A coding assistant logs every "regenerate" click; the team uses those traces as a hard test set for the next prompt iteration.
Frequently asked
What is the difference between Online Evaluation and User Feedback Loop?
Online Evaluation: Online evaluation runs scoring functions over live production traffic — usually a sample of recent traces — to monitor quality continuously instead of relying solely on a fixed offline dataset. User Feedback Loop: A user feedback loop ingests explicit signals — thumbs up/down, edits, regenerates, copy-to-clipboard — back into evaluation and fine-tuning, turning real usage into a continuous quality signal.
When should I use Online Evaluation vs User Feedback Loop?
After offline eval is solid and you have meaningful production volume. Stretch your eval coverage from a fixed set to a live one. User Feedback Loop applies when you are focused on evaluation.
Are Online Evaluation and User Feedback Loop the same thing?
No. Online Evaluation is evaluation; User Feedback Loop is evaluation. They are related but address different parts of the AI stack.