ModelRefs / A/B Testing (LLMs) — AI Glossary
A/B Testing (LLMs) — AI Glossary
Splitting production traffic between two model variants to measure which produces better user outcomes through controlled experimentation.
Overview
LLM A/B tests compare: different models (GPT-4o vs. Claude), prompt variants (system prompt A vs. B), temperature settings, or RAG configurations. Metrics: task completion rate, user satisfaction (thumbs up/down), conversation length, revenue impact. Requires sufficient traffic for statistical significance; typically 1,000–10,000 sessions per variant.
Reference details
| Topic | ecosystem |
|---|---|
| Also known as | model A/B test, prompt A/B test |
| Last reviewed | 2026-06-24 |
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Frequently asked questions
What is A/B Testing (LLMs)?
Splitting production traffic between two model variants to measure which produces better user outcomes through controlled experimentation.
Is A/B Testing (LLMs) the same as model A/B test?
Yes — model A/B test, prompt A/B test are common aliases for A/B Testing (LLMs).
What concepts are related to A/B Testing (LLMs)?
Closely related concepts include canary deployment, blue green deployment, evaluation harness.