In classic software, you write a function, you write a test, and you know if it passes or fails. AI, especially LLMs and agents, don’t play by those rules. Their outputs are probabilistic, context-sensitive, and non-deterministic. The same prompt can yield different answers, and “correctness” is often nuanced and qualitative, not quantitative in nature.
AI evals are structured, repeatable processes for measuring the quality, reliability, and safety of your AI applications. Evals are your compass. They help you navigate the messy, shifting landscape of real-world scenarios for your agents, ambiguous requirements, and evolving user needs. They’re not about chasing a single “accuracy” number, they’re about asking, “Is this system doing what we need, for our users, in our context?”
AI evals are structured, repeatable processes for measuring the quality, reliability, and safety of your AI applications. Evals are your compass. They help you navigate the messy, shifting landscape of real-world scenarios for your agents, ambiguous requirements, and evolving user needs. They’re not about chasing a single “accuracy” number, they’re about asking, “Is this system doing what we need, for our users, in our context?”
