Online vs Offline AI Evals: When to Use Each
Blog post from Inngest
In the evaluation of AI agents, offline and online methods serve distinct purposes and are often used in tandem to ensure robust performance monitoring. Offline evaluations involve testing the AI against a fixed dataset before deployment, functioning like unit tests to catch regressions and provide a controlled assessment environment. Conversely, online evaluations occur in real-time, assessing the AI's performance against live production data and user interactions, offering a dynamic and authentic measure of how the agent behaves in real-world scenarios. The combination of both methods allows teams to identify issues before release with offline evals, and to gain insights into the agent's real-time performance and user acceptance with online evals. The cost and complexity of these evaluations depend more on the scoring methods employed—such as algorithmic, signal-based, or LLM-as-judge—than on whether they are conducted online or offline.
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