Comparison
Offline Evaluation vs Regression Testing (LLMs)
Offline Evaluation and Regression Testing (LLMs) are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
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.
When you would reach for Regression Testing (LLMs)
As soon as you have repeat bugs or "we fixed that already, didn't we?" moments.
A PR template requires: zero regressions on the 30 must-pass examples; overall win rate ≥ 50%; cost per call within 10% of baseline.
Frequently asked
What is the difference between Offline Evaluation and Regression Testing (LLMs)?
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. Regression Testing (LLMs): LLM regression testing is the practice of running every prompt or model change against a fixed set of "must-pass" examples — bug repros, edge cases, known failure modes — to catch quality regressions.
When should I use Offline Evaluation vs Regression Testing (LLMs)?
Offline Evaluation is the right concept when you are focused on evaluation. As soon as you have repeat bugs or "we fixed that already, didn't we?" moments.
Are Offline Evaluation and Regression Testing (LLMs) the same thing?
No. Offline Evaluation is evaluation; Regression Testing (LLMs) is evaluation. They are related but address different parts of the AI stack.