Konstantin Semenenko
July 17, 2026
4
minutes read
Evals are the tests for AI systems: structured ways to measure whether a model or agent produces good outputs, since you cannot check non-deterministic text with pass/fail assertions. A real eval suite combines a dataset of representative inputs, scoring methods (programmatic where verifiable, LLM-as-judge for open-ended output, human review for the hardest cases), and a tracked metric. Without evals, "the AI got better" is a guess. Build them early, score on your real task, and treat them as the regression tests that let you change an AI system with confidence.
AI evals are the tests for AI systems: structured, repeatable ways to measure whether a model or agent produces good outputs. They exist because you cannot test AI the way you test normal software. Traditional tests assert exact outcomes, this function returns 4, but an AI system produces non-deterministic natural language, where the same input can yield different valid outputs and "correct" is often a matter of degree. So evals replace pass/fail assertions with measurement: a dataset of representative inputs, one or more scoring methods, and a metric you track. Without them, every claim about an AI system, this prompt is better, this model improved things, the agent is reliable enough to ship, is a guess. With them, it is a measurement you can defend and a regression suite you can change the system against. This explains what evals are and how to build ones that actually tell you something.
We build the verification and evaluation layer that decides whether an AI system is good enough to ship, so this is a practitioner's guide to evals and where they go wrong.
Normal software is deterministic: given an input, it produces a defined output, so a test asserts that output and passes or fails. AI systems break both assumptions. They are non-deterministic (the same prompt can produce different outputs), and their outputs are often open-ended text where correctness is not binary, a summary can be good, adequate, or wrong in ways no exact-match assertion captures. Asserting output == "expected" on a language model is mostly useless, because there are many valid outputs and the model rarely produces a byte-identical one.
This is why "it worked when I tried it" is not testing an AI system, it is one sample of a distribution. An AI feature that works on the three inputs you tried can fail on the fourth, and you will not know until a user hits it. Evals solve this by measuring behavior across many representative inputs and scoring the outputs by quality rather than exact match, turning "seems to work" into "scores X% on this suite." That shift, from anecdote to measurement, is the entire point of evals, and it is what lets you actually know whether an AI system is good and whether a change helped.
A useful eval has three parts, and skimping on any one weakens it:
Together these make an eval a real instrument: run the dataset through the system, score the outputs, get a metric, and compare it across versions. The most common mistake is testing on a handful of cherry-picked inputs, which measures nothing, or scoring only on easy cases, which hides the failures that matter. A good suite is representative, scored appropriately for the output type, and tracked.
The hardest part of evals is scoring non-deterministic, open-ended output, and it is worth understanding the options. Where an output is verifiable, extract it and check programmatically (did it return valid JSON, the right classification, a number in range), this is cheap and reliable, so use it wherever the task allows. Where the output is open-ended (a summary, an answer, generated content), exact-match fails, and the practical tool is LLM-as-judge: a separate model scores the output against a rubric you define. It scales and correlates reasonably with human judgment when the rubric is clear, but it has real limits, it inherits model biases and can be gamed, so it is a strong tool, not a perfect one.
For the highest-stakes or most subjective cases, human review remains the gold standard, and the right design uses it deliberately: automate scoring where you can, and route the cases you do not trust automated scoring on to humans. This mirrors the code-review discipline in AI code review: independent, layered checking, cheap automated passes plus human judgment where it counts. The failure mode to avoid is trusting LLM-as-judge blindly on everything, its convenience makes it tempting to over-rely on, and a judge that is subtly wrong gives you confident, wrong metrics.
Evals are not a nice-to-have you add before launch, they are what make an AI system improvable and trustworthy. Without evals, you cannot answer the questions that matter: did this prompt change help or hurt? Is the new model actually better for our task? Is the agent reliable enough to ship? Is it getting worse over time as the world drifts? Every one of these is a measurement question, and evals are the measurement. Teams without them fly blind, changing prompts and models on vibes and shipping regressions they cannot see.
This is the same principle as verification in agent systems, our MCAF framework is built on the idea that AI output must be verified against evidence, not trusted because it looks right, and evals are that verification applied to system quality. They are the regression tests that let you change an AI system with confidence, the metric that turns "the AI seems good" into a number, and the early-warning system that catches quality drift before users do. For any AI system you intend to run and improve, evals are the foundation everything else stands on.
AI evals are the tests for AI systems: structured measurement of output quality, because you cannot assert exact outcomes on non-deterministic text. A real eval suite is a representative dataset, appropriate scoring (programmatic where verifiable, LLM-as-judge for open-ended output, human review for the hardest cases), and a tracked metric. They matter because without them every claim about an AI system is a guess, and with them you get a regression suite that lets you change prompts, models, and agents with confidence and catch quality drift early. Build evals early, score on your real task with the right method for each output type, and treat them as the foundation, an AI system you cannot measure is one you cannot safely improve.
If you want an AI system built with real evals, measured, regression-tested, and trustworthy to change, that is where our AI Dev Team work starts.
What are AI evals? Structured, repeatable tests for AI systems that measure whether a model or agent produces good outputs. Because AI output is non-deterministic and often open-ended, evals replace exact-match assertions with measurement: a dataset of representative inputs, scoring methods, and a metric tracked over time.
Why can't you test AI like normal software? Because AI is non-deterministic (the same input can yield different valid outputs) and its outputs are often open-ended text where correctness is a matter of degree, not binary. Asserting exact equality on a model's output is mostly useless, so you measure quality across many inputs instead of asserting one exact result.
What is LLM-as-judge? A scoring method where a separate model rates an AI system's output against a defined rubric, used for open-ended outputs (summaries, answers, generated content) where no exact answer exists. It scales and correlates reasonably with human judgment when the rubric is clear, but it inherits model biases and can be gamed, so it is a strong tool, not a perfect one.
What makes a good eval suite? A representative dataset drawn from real usage covering hard and edge cases, appropriate scoring for each output type (programmatic where verifiable, LLM-as-judge for open-ended, human review for high-stakes), and a tracked metric so you can tell whether changes help or hurt. Testing a few cherry-picked inputs measures nothing.
Why are evals important for AI systems? Because without them, every claim, this prompt is better, this model improved things, the agent is reliable enough, is a guess. Evals turn quality into a measurable number, act as regression tests so you can change the system with confidence, and catch quality drift before users do. They are the foundation of a trustworthy, improvable AI system.
