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AI evals, short for evaluations, are structured tests built to measure how well a model or an AI system performs on a defined set of tasks, using some consistent way to score the results. An eval usually has three parts: a set of inputs, often questions or scenarios chosen to represent what the system will actually face, an expected standard for what a good response looks like, and a scoring method, which might be an exact match check, a rubric a human applies, or another model asked to grade the output. Run the same eval against two versions of a system and you get a comparable measure of which one did better on the thing the eval was designed to test.
Evals exist because it is genuinely hard to tell whether a change to a prompt, a model, or a whole pipeline made things better or worse just by trying it a few times and seeing how it feels. Language model behavior is variable enough, and the range of things users actually ask is wide enough, that a handful of manual checks can easily miss a regression that only shows up on a category of input nobody happened to try. Evals turn that fuzzy impression into a number, or a set of numbers, that can be tracked over time and compared across changes.
What separates a real eval from just eyeballing outputs is that it has to be run consistently, on a fixed or representative set of cases, with a scoring approach that gives comparable results across runs. Trying a new prompt on three examples and deciding it looks better is not an eval, it is an impression, and impressions are notoriously unreliable at catching the specific kind of failure that only shows up once in every twenty tries. A real eval is built to catch exactly that, by testing enough cases with a consistent standard that a rare failure has a real chance of showing up in the numbers.
By 2026, running evals has become a standard part of building any AI product taken seriously, sitting alongside more traditional software testing rather than replacing it, with teams building eval suites early and treating a drop in eval scores the way engineers treat a failed test in a regular codebase. It has also matured past simple exact-match scoring toward more nuanced grading, including using one model to judge another's output against a rubric, which has made evals workable for the open-ended, harder to score tasks that language models are increasingly used for.
This page covers how evals are actually built and run, how they compare to traditional software testing, and where they fit against watching a live system in production versus testing it beforehand. The idea to hold onto is that an eval is only as good as what it measures and how honestly it is scored. A shiny score on a badly designed eval tells you almost nothing useful about the real product, and the hard part of evals has always been deciding what to test and how to score it fairly, not the comparatively mechanical work of actually running the test itself.
The starting point is a set of test cases, inputs meant to represent the real range of things the system will be asked to handle, including both common requests and the tricky edge cases that tend to expose weaknesses. Building a good set of cases is often the hardest and most time consuming part of the whole process, since a lazy or narrow set of cases will pass a system that still fails badly on inputs the eval never thought to include.
Each case usually needs some notion of what a correct or good response looks like, whether that is an exact expected answer, a rubric describing what a strong response should contain, or a reference answer used for comparison rather than exact matching. For tasks with one clearly right answer, this part is simple. For open-ended tasks like writing or summarizing, defining what good even means is a real design problem on its own, separate from the scoring mechanism used to apply it.
Scoring then applies that standard to the system's actual output, using methods that range from a simple automated check, like whether a required keyword appears, to a human rater reading the response and applying a rubric, to another language model asked to grade the response against defined criteria. Each method trades off cost, speed, and reliability differently, and many serious eval setups end up using a mix, cheap automated checks for the bulk of cases and human review for a smaller sample used to sanity check the automated scores.
The results get aggregated into scores that can be tracked over time and compared across versions of a system, ideally broken down by category of input rather than one single overall number, since an average score can hide a system that is great on common cases and quietly terrible on a specific category that matters a lot to real users. Teams that only look at the overall average tend to miss exactly the regressions evals were built to catch, which defeats much of the purpose of running them in the first place.
Traditional software tests usually check for exact, deterministic behavior: given this input, the function should return exactly this output, and if it does not, the test fails clearly and unambiguously. This works well because most traditional software is deterministic, meaning it produces the same output every time given the same input, so a pass or fail result is meaningful and repeatable. Decades of software testing practice were built on that assumption, and much of it simply does not transfer cleanly once the assumption breaks down.
AI evals have to deal with outputs that are often not deterministic and rarely have one single correct answer, especially for open-ended tasks like writing or reasoning through a problem. A good response and a great response might both be reasonable, and a technically correct response can still be unhelpful in tone or structure, none of which a simple pass or fail check captures the way a traditional unit test can for a function with one right output. This is exactly why eval scoring has had to grow more sophisticated than a simple equality check.
This forces evals to lean more on scoring scales and rubrics than binary pass or fail results, and to accept that scores will shift somewhat between runs even without any actual change to the system, simply because of the inherent variability in how language models respond. A traditional test suite that is red one run and green the next with no code changes usually points to a broken test. An eval suite doing the same thing might just be reflecting normal variability, which is a much harder thing to distinguish and tune for.
Despite these differences, the two disciplines are converging rather than staying separate, with eval suites increasingly run in the same continuous integration pipelines as traditional tests, and teams borrowing testing discipline, like version controlling test cases and tracking historical results, directly from how software testing has worked for decades. Treating evals as an entirely separate, lesser discipline from testing tends to mean they get built later and taken less seriously than they should be, which shows up as regressions that a proper test would have caught.
A benchmark is typically a fixed, public, standardized test used to compare different models or systems against each other on a common yardstick, often published by a research group or a lab and reused widely across the industry as a shared point of reference. Benchmarks are built to generalize, meant to say something about a model's capability broadly, independent of any one company's specific use case. That generality is the point, since a benchmark is meant to say something useful about a model no matter who ends up building on top of it.
An eval, by contrast, is usually built by a specific team for their specific system and their specific users, testing exactly the tasks that system needs to handle well, which a generic benchmark was never designed to capture. A model that tops a public benchmark can still perform poorly on a narrow, specialized task that the benchmark never tested, because the benchmark's scope and a particular product's needs are simply not the same thing. A benchmark score is a starting hint about a model's general ability, not a promise about how it will behave inside your specific application.
The two serve different purposes and neither replaces the other. Benchmarks are useful for picking which underlying model to build on in the first place, giving a rough sense of general capability before you have invested in building anything specific. Evals are useful once you have an actual system built on top of that model, checking whether your particular application, with your particular prompts and your particular users, actually works well, which a benchmark score can never tell you directly.
Confusing the two leads teams to trust a high benchmark score as if it guarantees good performance on their own product, which it does not. A team that never builds its own evals and relies purely on public benchmark rankings to judge whether their system is good enough is making a decision based on a test that was never about their system in the first place, and that gap tends to surface only once real users start complaining about something the benchmark never measured.
Evals fit well anywhere a team needs to compare two versions of a system, a new prompt against an old one, one model against another, or a change to an agent's tools against the previous setup, and wants a repeatable, comparable answer rather than a subjective impression from trying each one a few times. They also fit well as a gate before shipping changes, catching regressions before they reach real users rather than discovering them from complaints afterward, which is a far cheaper place to catch a problem than in a support queue.
They fit especially well for tasks with enough volume or enough consequence that a manual review of every change is not realistic or not safe, since that is exactly the situation a structured, repeatable test is built to help with, giving confidence at a scale a human reviewer could not realistically match on their own, no matter how careful and diligent that person tried to be about checking every single case that came through the pipeline on a given day.
Evals fit poorly, or need real care, on tasks that are so open ended or so tied to individual taste that defining a clear standard of good is itself the hard problem, since a poorly designed rubric can produce a confident, precise looking score that measures the wrong thing entirely. A number that looks rigorous is not automatically a number that means something, and that false sense of confidence is often worse for a team than simply having no score to look at all.
They also fit poorly as a total substitute for watching a live system, since a test suite, however well built, only covers the cases someone thought to include, while real users reliably find inputs nobody anticipated. Evals reduce the chance of shipping an obvious regression, but they do not replace the need to keep watching what actually happens once a system is live and handling traffic evals never modeled, which is exactly the gap that ongoing observability is built to close over time.
Build your test cases from real examples wherever possible, drawing on actual user requests, actual failures the team has seen, and known edge cases, rather than inventing hypothetical examples from scratch. Real cases tend to expose real problems, while made-up cases too often reflect what the team already expects rather than what actually happens once a system is live and being used by people the team never anticipated in quite the way those real situations end up showing up in practice.
Break scores down by category rather than relying on one overall number, since an average can hide a system that handles common requests well while quietly failing on a specific, important slice of traffic. Knowing which category is weak is far more useful for fixing anything than simply knowing the overall score moved a few points in one direction or the other across the last several runs of the same eval suite, since the average tells a team almost nothing about where to actually focus its next fix.
Match your scoring method to the task rather than defaulting to whatever is easiest to set up. Simple exact-match scoring works for tasks with one right answer, but forcing it onto an open-ended writing or reasoning task will produce numbers that look precise while measuring almost nothing real about quality, which is often worse than having no metric at all since it invites false confidence in a decision that genuinely deserved a harder, more honest look before anyone signed off on it.
Refresh your eval set over time instead of treating it as fixed forever, adding new cases as real failures show up in production and retiring cases that stopped being relevant as the system and its users changed. An eval suite from a year ago can quietly stop reflecting what the system actually needs to handle today, giving a team false confidence right at the moment it matters most for a real decision about what to ship out to users next.
Use evals alongside observability rather than instead of it, treating real production failures as a source of new test cases and using evals to confirm that a fix for a production problem actually holds before it goes back out. Neither discipline on its own gives a complete picture of how well a system is actually doing once real users, with all their unpredictability, are genuinely involved in using it day after day for their own purposes and in their own words.
AI evals are structured tests that measure how well a model or AI system performs on a defined set of tasks, using a consistent scoring method so results can be compared across versions and over time as changes get made.
They turn a fuzzy impression of whether a change helped or hurt into a repeatable, comparable measurement, which manual spot checks often miss, especially for failures that only show up on certain kinds of input the team did not happen to try.
Benchmarks are fixed, public tests used to compare general model capability across the industry. Evals are built by a specific team to test their own system against their own users' actual needs, which a generic benchmark was never designed to capture.
Traditional tests usually expect one deterministic correct output. AI evals often deal with non-deterministic, open-ended outputs, so they rely more on scoring scales and rubrics than a strict pass or fail check that a normal test suite would typically use for comparison.
Scoring can come from simple automated checks, human raters applying a rubric, or another language model asked to grade the output against defined criteria, and many teams combine all three depending on cost, speed, and the stakes of the task.
No. Evals only test the cases someone thought to include beforehand, while real users reliably produce inputs nobody anticipated, so evals and production observability work best together as complements rather than as a substitute for one another over the long run.
Regularly. New cases should be added as real failures show up in production, and old cases should be retired once they stop reflecting how the system and its users actually behave today, rather than left in place indefinitely simply out of habit or convenience.
Not necessarily. A model can rank well on a general public benchmark and still perform poorly on a narrow task specific to a particular product, which is exactly what a tailored eval is meant to catch before it reaches real users.