Offline Evals to Online Experiments

Offline evals to online experiments is the AI-product workflow of testing prompts, models, context, or app versions against representative inputs before shipping the best candidates into live A/B tests.

Key points

• Statsig contrasts older academic ML testing with a foundation-model workflow where teams prepare representative inputs, run them through candidate LLM-app versions, and review output quality ^[src-032].
• Offline evals are useful for comparing prompts, models, context, and other application components before exposing users to the change ^[src-032].
• The article warns that separate eval apps can miss important product context, such as prior chat history, UI elements, and other experience components that interact with prompts or models ^[src-032].
• A growing pattern is to run eval-like checks inside a production version or representative prototype, then ship the candidate as an A/B test for real user signal ^[src-032].
• Online experiments add evidence that offline evals cannot provide alone, including effects on product behaviour, cost, latency, and broader user outcomes ^[src-032].

Related entities

• Statsig

Related concepts

• AI Product Experimentation
• Experiment Iteration Loop
• A/B Test Acceleration
• Proxy Metrics in Experiments
• Offline Policy Evaluation

Source references

• ^[src-032] Skye Scofield and Sid Kumar — “Experimentation and AI: 4 trends we’re seeing” (2025-06-13)