Model evaluation means testing an AI model or system on a fixed set of tasks to see how accurate, safe, fair or reliable it is. Testers use public benchmarks, test sets built from the real job the tool will do, and red teaming. It works like a trial shift for a new hire. Before you trust someone with donor letters, you watch them write a few and check the results.
Why it matters
Vendors often point to high scores on public leaderboards, but those tests may look nothing like your work. For a small organization, a short test with your own typical tasks tells you more. Pick 10 or so real requests, such as summarizing a grant report or answering a common client question. Run them through the tool, and have someone who knows the subject check every answer for mistakes and hallucination. When you talk to a vendor, ask how they evaluated the model, which tasks they tested, and whether the results appear in a model card. If they cannot answer clearly, treat that as a warning sign.
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