AI evaluation is the process of measuring how good an AI model or AI-powered assistant actually is. It uses test questions with known good answers, scoring methods and sometimes human reviewers to judge accuracy, helpfulness, safety, speed and consistency.
Public benchmarks are standard tests used to compare models on skills such as reasoning, coding or general knowledge, and they appear in leaderboards and vendor announcements. They are useful for a rough comparison but may not reflect your tasks, and models can be tuned to perform well on them.
The evaluation that matters most to a retailer or venue is your own: a set of real customer questions, edge cases and trick requests with the answers you would approve. Run it before launch, after every prompt or model change, and on a sample of live conversations. Score for correct information, tone, refusal of out-of-scope requests and handover when needed.
Pitfalls include relying on a vendor demo, testing only easy questions, and treating a good score as permanent, since behaviour changes as models and your content change. Keep a record of results so that you can compare versions and spot regressions.
Evaluation turns "it seems fine" into evidence, and it is the main safeguard that lets you trust an AI assistant with real customers.