When Evaluation Should Intervene and When It Shouldn’t

At this point, evaluation should be less abstract and more integrated into the system’s operation. Teams can inspect full traces, surface real failures, and translate those failures into concrete evaluators. At this stage, many teams reach a turning point: problems are visible, learning is structured, and progress is no longer a matter of guesswork.

This is also where a new kind of confusion appears. Once you can reliably detect failures, it becomes tempting to stop them automatically. If you can judge a response as bad, why let it reach the user? If an evaluator can spot a risky output, why not block it or regenerate immediately? This lesson focuses on that boundary. It clarifies the difference between evaluators and guardrailsA guardrail is a fast, inline check that runs during the request–response path to block a small set of clear, high-impact failures before they reach the user., explaining why mixing them up can cause real damage, and how to determine which checks belong in the critical path vs. the learning loop.

What is the difference between a guardrail and an evaluator?

The most important distinction is where the check runs and what it is responsible for. Guardrails run inline, directly in the request–response path, before an output is shown to the user. Their job is to prevent a narrow set of clear, high-impact failures from escaping in real time. Evaluators, by contrast, usually run after the response has already ...