Comparison
Drift Detection vs Hallucination
Drift Detection and Hallucination 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 Hallucination
Hallucination comes up when the question is fundamentally about evaluation.
Citing a paper that does not exist.
Frequently asked
What is the difference between Drift Detection and Hallucination?
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. Hallucination: A hallucination is a confidently-stated, plausible-sounding LLM output that is factually wrong. It is the failure mode that most often surprises non-expert users.
When should I use Drift Detection vs Hallucination?
Once you have stable traffic and a baseline; drift detection is most valuable in mature production systems. Hallucination applies when you are focused on evaluation.
Are Drift Detection and Hallucination the same thing?
No. Drift Detection is infrastructure; Hallucination is evaluation. They are related but address different parts of the AI stack.