Offline vs. Online Evaluation
Explore the concepts of offline and online evaluation within AI system development. Understand how offline evaluation tests AI before deployment using a curated Golden Dataset, and how online evaluation monitors live user interactions with real-time guardrails and asynchronous monitors. Learn to integrate these methods into a continuous loop that improves your AI system by converting production failures into permanent tests, enhancing system reliability and scalability.
Throughout this course, you have learned to evaluate systems by analyzing traces, generating synthetic data, and identifying failure bundles. You have built the “muscle” of evaluation. Now, we need to organize that muscle into a functional production workflow.
If you search for “LLM Ops” or “AI Engineering,” you will frequently see evaluation divided into two distinct categories: offline evaluation and online evaluation.
These terms describe the environment where the evaluation occurs. Offline evaluation runs in your development environment before you ship, while online evaluation runs within the live product while users are interacting with it.
A mature AI team does not choose one over the other. Instead, they build a continuous loop that connects them. This post explains how to operationalize that loop using the artifacts you have already created.
Feature | Offline Evaluation | Online Evaluation |
Where it runs | CI/CD Pipeline (Development) | Production (Live Traffic) |
When it runs | Before deploying changes | During/After user interaction |
Data Source | Curated Golden Datasets | Real User Inputs |
Goal | Catch regressions | Catch new/unexpected behavior |
Cost | Low (Simulated runs) | High (Real user impact) |