AI Evals - Testing AI Applications Systematically

AI Evals (short for "AI Evaluations") refer to the systematic testing of AI and LLM applications against a predefined test set with clear evaluation criteria. Instead of judging an AI output as merely "good" or "bad" by gut feeling, every answer is checked against measurable criteria - reproducibly and repeatably.

The reason lies in the nature of generative models: they are non-deterministic. The same input can produce different answers, and a small change to the prompt or a model update can shift behavior in places you were not watching. Without structured evaluation you only notice this once users report it.

Why "Just Trying It Out" Is Not Enough

• The sample deceives: A few manual test questions cover only a tiny slice of the possible input space. What works for five examples may fail for the next fifty.
• Non-determinism: Because the same input can produce different outputs, a one-time "it worked" is no proof of reliable behavior.
• Silent regression: After a prompt or model change, quality can degrade in unexpected places. Without a test set this goes unnoticed until it gets expensive.

Building Blocks of an Eval Setup

1. Test set: A curated collection of inputs, ideally with an expected result or reference answer. It should contain typical cases and known problem cases.
2. Evaluation criteria: Clearly defined metrics - such as correctness, completeness, adherence to a format, or the absence of undesired statements.
3. Scoring method: Automatic by rule and comparison, via LLM-as-a-Judge, or by human review - depending on the criterion and the effort.
4. Repetition: The run is repeated on every change so that improvements and regressions show up as numbers.

Offline vs. Online Evals

• Offline evals run before release against a fixed test set - comparable to automated tests in development. They answer the question: is the new version at least as good as the old one?
• Online evals measure behavior in live operation on real user requests, often based on signals such as user feedback or downstream success metrics. They are closely tied to LLM observability.

How It Differs From Classic Software Testing

A classic software test checks deterministic behavior: for the same input, exactly the same output is expected, and the check is binary - pass or fail. AI Evals move along a spectrum. Answers are rarely word-for-word identical but more or less fitting, and the assessment often needs a judgment of meaning rather than a character comparison. An eval suite therefore complements the classic test but does not replace it.

From Single Answer to Agent

AI Evals first assess single model answers. As soon as several steps, tools, and decisions come together, that is no longer enough - then it becomes a matter of agent evaluation, which also checks the flow and tool use of a AI agent. Both belong to clean operation as described by AgentOps.

AI Evals at Elasticbrains

At Elasticbrains we build eval suites that fit your application - from the test set and the evaluation criteria to the question of which check runs automatically and which is done by humans. See how we embed this into ongoing operation on our service page testing & operating AI agents.