Online vs. Offline AI Evals: When to Use Each (www.inngest.com)

Recent insights highlight the importance of both online and offline evaluations (evals) for AI agents, emphasizing their distinct roles in quality assurance. Offline evals use a fixed dataset to test an AI agent’s performance before deployment, acting like unit tests that catch regressions by assessing known cases. Conversely, online evals continuously score each interaction in real-time with actual user data, enabling teams to observe how agents perform in the wild. This dual approach ensures robust performance, as offline evals can catch predictable issues, while online evals offer insights into unforeseen scenarios and real-world outcomes. The article provides a breakdown of key components for effective AI evaluations, including dataset selection, scoring mechanisms, and the significance of defining scoring rubrics. It discusses how using live data for online evals offers a higher volume and accuracy of feedback but also highlights the costs associated with scoring live traffic. Ultimately, integrating both evaluation methods allows teams to improve their AI systems comprehensively, measuring performance before and after deployment, thus ensuring agents operate effectively and meet user needs.