In AI, “Evals” refers to evaluations or tests designed to assess the performance, capabilities, and safety of AI models. These evaluations range from basic tests that measure an AI’s accuracy on specific tasks to more comprehensive assessments of its ability to understand complex instructions, reason logically, and respond safely in various situations. Evaluations are crucial for understanding how an AI performs both on intended tasks and in scenarios it might encounter in the real world.

Evals often come in two primary types: benchmarking tests and customized evaluations. Benchmarking tests involve standardized datasets and tasks, allowing researchers to compare models easily. For example, a benchmark might evaluate how well an AI processes natural language, handles image recognition, or generates responses in a chatbot format. Customized evaluations, on the other hand, are created to test specific goals, such as ensuring an AI adheres to ethical guidelines or handles sensitive information responsibly.

As AI systems grow more complex, so do their evaluation processes. The most advanced evals measure not only functional performance but also qualities like fairness, bias, robustness, and interpretability. These characteristics are critical for ensuring that AI behaves as intended, even in unexpected situations or when it encounters unusual input.