Choosing Between Online and Offline AI Evals
anshulkundaje · x · 2026-07-16
This article discusses the appropriate use cases for online evals versus offline evals.
The core takeaway includes:
- Offline evals are better suited for systematic comparisons prior to release, making them ideal for reproducibility and regression testing
- Online evals are better for observing real-world user distributions, long-term behaviors, and product performance post-launch
- The two solve different problems and cannot replace each other
The title highlights a highly practical engineering topic: how to build a more reliable evaluation loop for AI systems rather than relying on a single benchmark.
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