How should evaluators test AI systems (as opposed to models)?
While much of AI evaluation research focuses on models, users experience AI through complete systems—applications layered with retrieval, guardrails, tools, and autonomous capabilities. This changes how systems should be tested. In this post, part of a multi-blog series from the NAAIMES Network, rigorous AI system evaluation is explored: realistic testing environments, representative datasets, appropriate metrics, and meaningful thresholds to assess whether AI systems are truly fit-for-purpose and safe for real-world deployment. This blogpost introduces the technical, operational, and domain expertise that evaluators need to develop to conduct effective AI system assessments.
