AI evaluation vs testing is the difference between checking code paths and grading open-ended behavior. Your unit tests can be green while your model confidently invents facts, because tests and evaluations answer different questions.
We grade outputs. We do not train models, run fine-tuning, or sell experts we cannot verify.
Testing asserts deterministic behavior: given this input, the code must produce exactly this output. Evaluation grades probabilistic behavior: given this input, is the model's output good enough, and how bad is it when it is not.
Software testing assumes the same input gives the same output every time. Language models do not work that way. The same prompt can produce a correct answer, a wrong answer, and a beautifully written hallucination on three consecutive runs.
So testing tells you the system works as built. Evaluation tells you whether what it built is any good. You need both, and this page explains where each one earns its keep. Our LLM evaluation service covers the second half.
None of these are testing failures. They are category errors: asking a deterministic tool to judge probabilistic output.
Tests assert exact matches. Models sample from a distribution, so the same prompt returns different wording every run, and sometimes different facts. A test suite built on exact matching either flakes constantly or gets loosened until it checks nothing.
Unit tests cover branches in your code. But the failure surface of an LLM app is the space of possible outputs, which is effectively infinite. You cannot branch-cover your way to catching a hallucinated citation. You have to sample outputs and grade them.
A test passes or fails. A model output can be slightly off, badly wrong, or dangerously wrong, and those are different situations that need different responses. Evaluation grades on a scale, like our P0 to P3 severities, so teams can prioritize.
Tests catch broken builds: something that worked yesterday and fails today. Models also fail slowly, with quality drifting down across prompt tweaks and model updates that no test flags. Evaluation catches drift because it measures output quality directly, run after run.
You send 20 to 50 model outputs plus your rubric, or we help you write one.
Trained reviewers grade every sample against the rubric, backed by automated checks.
Sample-level grades, issue patterns, recommended fixes, and a walkthrough call.
Keep your tests. Everything deterministic in your pipeline should be tested the usual way: API contracts, data parsing, prompt assembly, tool calling, output schemas, latency budgets. If a component has exactly one right answer, write a test for it.
Testing is also the right tool for your guardrails themselves. If you add a filter that blocks certain content, test that the filter fires. Evaluation then checks the harder question: does the filter catch the real cases without blocking good outputs?
Evaluation starts where exact answers end. Open-ended generation, summaries, RAG answers, agent tool use, tone and helpfulness: all of these need graded samples, not assertions. The method is simple to describe and disciplined to run: sample outputs, grade them against a rubric, track patterns.
Our complete guide to LLM evaluation walks through the full method. The short version: 20 to 50 samples, a written rubric, every sample graded with a reason, and a report your team can act on.
The practical setup is a pipeline with two gates. Tests run on every commit and block broken builds. Evaluations run on a schedule or before releases and block quality regressions. Different cadence, different questions, same goal: nothing bad reaches users.
Teams that do this stop arguing about whether a change "felt" worse. The eval scores say whether it was worse, by how much, and on which samples. That turns model updates from anxious guesses into measured decisions.
Only the deterministic parts around it. You can test that your code assembles the prompt correctly, calls the model, and parses the response. You cannot unit-test whether the response itself is good. That needs sampled outputs graded against a rubric, which is what evaluation does.
Snapshot tests lock in whatever the model said once and flag any change. That catches drift, but it also flags every harmless rewording and bakes in whatever quality the snapshot had, good or bad. They are a tripwire, not a grade. Useful as an alert, useless as a verdict.
More often than you think, less often than your tests. A common cadence: a full graded batch before any model or prompt change ships, plus a smaller recurring sample to catch drift. Teams shipping weekly usually evaluate weekly. The cost of a batch is small next to the cost of a bad release.
Yes. Evaluation does not check your API contracts, your data parsing, or your latency. Tests do that cheaply and instantly. Evaluation checks output quality, which tests cannot see. Dropping either one leaves a blind spot.
Start with a pilot: 20 to 50 real outputs from your current system, graded against a simple rubric. That one batch usually surfaces the failure patterns your tests never saw, and it gives you a baseline to measure every future change against.
Send us a batch of real outputs and we will grade them.
Not sure where to start? Talk to us and we will point you at the right evaluation.