AI Evaluation
AI evaluation is measuring how well a model or system performs on defined criteria such as accuracy, safety, or alignment with instructions.
In Simple Terms
Think of it as a report card for your AI: grades on the dimensions that matter for your product.
Detailed Explanation
Evaluation uses benchmarks, human review, or automated checks to score outputs. It is essential for shipping reliable AI and for comparing models or prompts. When to use it: before launch, after changes, and when comparing options. Common mistakes: evaluating only on one metric, or using benchmarks that do not match real use cases.
Related Terms
Model Benchmarking
Model benchmarking is the practice of evaluating and comparing AI models on standardized tasks and datasets to measure performance, accuracy, and capabilities. Results help practitioners choose the right model and track progress over time.
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