What is AI evaluation?
AI evaluation tests whether a model or AI product gives useful, safe, accurate answers on the tasks that matter.
The short version
AI evaluation, often called evals, is how teams measure AI quality. Instead of trusting demos, they build test sets, score outputs, review failures, and compare models or prompts against real use cases.
Why evals are hard
AI answers are open-ended. Two answers can be different but both acceptable, or polished but wrong. Good evals need clear rubrics, representative tasks, adversarial cases, and sometimes human review.
Types of evals
Teams use factual accuracy tests, citation checks, safety tests, coding tests, regression suites, user feedback loops, latency checks, cost tracking, and model-graded reviews.
What evals can miss
A benchmark can overfit to public tasks, miss new user behavior, or reward style over correctness. Passing an eval does not mean the system is safe in every situation.
What strong teams do
They track failures, add regression cases, compare against baselines, and measure production feedback. The best evals evolve as users reveal new edge cases.