Comparison
Drift Detection vs Online Evaluation
Drift Detection and Online Evaluation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Drift Detection
Once you have stable traffic and a baseline; drift detection is most valuable in mature production systems.
A support bot's P50 latency jumps 30%: a drift alert fires on the input distribution, revealing users started pasting in entire emails instead of short queries.
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.
Frequently asked
What is the difference between Drift Detection and Online Evaluation?
Drift Detection: Drift detection watches for changes in the statistical distribution of inputs, outputs, or quality scores over time — so you can catch a model degrading in production before users complain. 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.
When should I use Drift Detection vs Online Evaluation?
Once you have stable traffic and a baseline; drift detection is most valuable in mature production systems. After offline eval is solid and you have meaningful production volume. Stretch your eval coverage from a fixed set to a live one.
Are Drift Detection and Online Evaluation the same thing?
No. Drift Detection is infrastructure; Online Evaluation is evaluation. They are related but address different parts of the AI stack.