QA verifies predictable behavior, where the same input gives the same output. A language model does not behave this way, so evaluation uses a different toolkit: reference examples, metrics and judge models. Your QA team can operate it after a handover, but the setup is a separate discipline.
LLM Evaluation Services
LLM evaluation is a way to systematically measure the quality of an LLM system. So you know your agent gives the right answer, chooses the right option, pulls from the right source.
Traditional QA doesn’t work with LLM systems
Regular software is predictable. The same input always produces the same output, so you can write down the result you expect and check whether you got it. An LLM works differently.
[1]
The same question gives different answers
Ask the same thing twice and you get two versions. Both might be right, both might be wrong. There’s no fixed result to compare against, so a normal test has nothing to hold on to.
[2]
You can’t see how it decided
You control what goes in, not the logic that runs on it. When an answer comes back wrong, there’s no line of code to find — only the output, and a guess about what caused it.
[3]
It gets worse without you noticing it
Providers release new versions and shift capacity between them. Your accuracy can drop while your code, your prompt and your model stay exactly the same.
Why do you need LLM Evaluation System
You can’t test a black box, but you can measure what comes out of it. That gives you four things.
[1]
Know your actual accuracy
A number, measured the same way every time. It tells you where your system stands today, and gives you a line you’re willing to ship above.
[2]
Control the cost
Run your own examples through several models and you’ll see which one is good enough for your job. Often a cheaper model performs just as well, and you can see that before you switch.
[3]
See how changes affect accuracy
Someone edits a prompt or adds a step. You know whether it helped, or whether it fixed one thing and quietly broke another.
[4]
Catch problems you didn’t cause
The same examples run on a schedule. If the system starts giving wrong answers after a provider updates a model, you see it in the results instead of hearing it from a customer.
Two evaluation systems we built
Two very different systems, the same question in both: how often is the answer actually right? In each case the team ended up with a number they could act on, and a way to keep watching it.
Spec Book AI Agent
The system checks whether the AI classifies documents correctly
[1]
The Spec Book AI Agent reads a construction specification and decides which of 36 materials fits.
To check its accuracy on a regular basis, we collected documents where the correct material is already known and confirmed by the client’s experts. The system goes through all of them, and the team sees the percentage of correct answers.
Multi-Agent Investment Platform
The system checks if the agents give correct answers and pull relevant documents
[2]
The multi-agent investment platform answers questions over 50,000 financial documents.
To check its accuracy, we prepared a set of 90 questions with reference answers. The report shows separately whether the system found the right documents and whether the answer composed from them is correct.
Our process
[1]
Evaluation audit
We review how your system works, prepare the ground truth dataset with your team and define what to measure.
[2]
Evaluation setup
We put the dataset, metrics and automated runs in place, and you see the accuracy of your system.
[3]
Ongoing checks
If needed, we set up ongoing checks. For example, when a new model comes out, we evaluate its performance against the current one to see whether switching is worth it.
Frequently Asked Questions
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Fewer than most teams expect. Eight to twenty carefully chosen examples already give a useful signal. The set then grows from user feedback.
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No. Evaluation runs against a test copy of your system. We agree on the prerequisites before the start: sample documents, a test environment and some time from a domain expert on your side.
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A named domain expert with a few hours per week, sample documents before the start, and a test environment we can send requests to. The availability of the expert is the strongest predictor of the timeline.
Let's collaborate
Tell us what your AI system is supposed to do, and we'll come back with how we'd measure it.
Reach out directly
[email protected]