AI Evals

Simple Definition

AI evals (short for evaluations) are structured tests used to measure how well an AI model performs on specific types of tasks. They help developers, researchers, and users understand a model’s strengths, weaknesses, and how it compares to other models.

If benchmarks are the standardized tests, evals are the broader practice of testing — including custom tests designed for specific use cases.

Why Evals Matter

You can’t improve what you can’t measure. Evals are how:

Researchers track whether a model is getting better over time
Developers know if a change improved or broke something
Businesses verify a model is reliable enough for a specific use case before deploying it
Users understand which model is best suited for their needs

Types of AI Evals

Capability evals — can the model solve math problems, write code, translate languages, answer questions accurately?

Safety evals — does the model produce harmful, biased, or misleading content?

Instruction-following evals — does the model do what it’s told, or does it go off-script?

Factual accuracy evals — does the model give correct, verifiable answers?

Human preference evals — do human raters prefer this model’s outputs over alternatives?

Examples of Common Benchmarks

MMLU — tests knowledge across many academic subjects
HumanEval — tests coding ability
GSM8K — math reasoning with word problems
GPQA — expert-level science questions
SimpleQA — factual accuracy testing

Limitations of Public Benchmarks

Models can be “trained on the test” — meaning the model was exposed to benchmark questions during training, inflating its score
A model that scores well on a benchmark may still fail at your specific use case
Real-world performance often differs from benchmark performance

The best eval for your needs is often a custom one built around your actual use cases — not just whatever the model’s benchmark card reports.