Comparison
Annotation vs Offline Evaluation
Annotation and Offline Evaluation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Annotation
Annotation comes up when the question is fundamentally about evaluation.
A team samples 200 production traces weekly, routes them to an internal Argilla instance, and has reviewers label correctness + a category tag.
When you would reach for Offline Evaluation
Offline Evaluation comes up when the question is fundamentally about evaluation.
A RAG team's offline eval: 500 (question, gold answer) pairs, scored by LLM-as-judge on faithfulness and relevance, run on every prompt PR.
Frequently asked
What is the difference between Annotation and Offline Evaluation?
Annotation: Annotation is the process of attaching ground truth or quality labels to data — by humans, sometimes augmented by an LLM. The unglamorous but decisive lever in LLM evaluation. Offline Evaluation: Offline evaluation runs a fixed dataset of inputs through a candidate model or prompt, scores each output, and reports aggregate quality — the standard way to compare changes before shipping.
When should I use Annotation vs Offline Evaluation?
Annotation is the right concept when you are focused on evaluation. Offline Evaluation applies when you are focused on evaluation.
Are Annotation and Offline Evaluation the same thing?
No. Annotation is evaluation; Offline Evaluation is evaluation. They are related but address different parts of the AI stack.