Definition

AI evaluation means checking how good or safe an AI system is. It looks at how accurate the results are, whether the model follows instructions, and how it behaves in real-world tasks. This helps developers improve the system and find issues before launch.

Example

AI evaluation might test how well a chatbot gives helpful answers without saying anything harmful.

How It’s Used in AI

Used in labs, research, and production to track AI performance. Evaluations look at things like bias, toxicity, reasoning skills, factual accuracy, and helpfulness. It’s key to shipping reliable AI and catching problems early.

Brief History

As LLMs became more capable and unpredictable, companies like OpenAI, Anthropic, and DeepMind started building formal evaluation teams and red-teaming processes to stress-test their models before release.

Key Tools or Models

Tools include OpenAI's eval frameworks, Anthropic’s AI evaluations, HELM, TruthfulQA, and internal tests on safety, reasoning, and task performance. Often used alongside alignment and red-teaming strategies.

Pro Tip

Evaluate early and often. Even small updates to a model can change how it behaves—especially with edge cases or ethical questions.