AI Evals and Observability: How to Check Whether an AI Actually Works

1. A helpful answer is not always a correct answer

Imagine a school library assistant. You ask it to reserve a science book. It replies, "Done!" But when you visit the library, no reservation exists.

The reply sounded helpful. The task still failed.

Artificial intelligence, or AI, needs checks that look beyond fluent writing. An evaluation, often shortened to eval, is a planned test with a clear definition of success. Observability is the information that helps us understand what happened inside the running application.

Think of a school experiment. Your marking guide says what a successful result looks like. Your experiment notebook records what you did. Evals are like the marking guide; traces are like the notebook. Neither replaces doing the experiment correctly.

Our running example is a library assistant that answers questions and reserves books. All workloads, thresholds, and results below are invented teaching examples.

2. Why ordinary tests still matter

A large language model, or LLM, produces language based on its inputs. Its answers can vary across runs, and changes to prompts, retrieved information, tools, or the model can change behavior.

Keep ordinary software tests. A reservation must still belong to the correct user. A date parser must still reject invalid dates. Add evaluations for behavior that cannot be captured by checking one exact sentence.

AI evals can produce binary results, numerical scores, or both. Traditional testing also includes statistical and performance testing. The useful distinction is what we need to measure, not which team is allowed to test it.

Here is the assistant we are about to evaluate, as it runs in production: