What Are LLM Evals? Measuring AI Output and LLM-as-a-Judge

What are LLM evals?

LLM evals are a systematic way to measure the answers of an AI application against a predefined test set and clear criteria. In short, the word eval stands for evaluation. The goal is to show, in a repeatable way, that the application works well instead of just feeling that it does.

Here is a simple analogy. In a restaurant kitchen, the head chef can taste every plate when orders are few. However, once orders grow, that stops working. So the kitchen adds recipe cards, tasting criteria, and random spot checks. Evals are the recipe card and the quality check of an AI application.

In this guide, we explain the term step by step. First we look at why evals matter, then at test sets and criteria. After that we cover the LLM-as-a-judge approach and the biases of a judge model. Finally we compare evals with benchmarks and share a practical checklist. In short, this is a conceptual and practical answer to what are LLM evals.

What are LLM evals, and how do they differ from regular software tests?

In classic software testing, the same input always produces the same output. If you give an addition function two and three, the answer is five, and a test checks that easily. A language model, however, behaves differently. It may, for example, give slightly different answers each time, and several of them can still be correct.

For that reason an eval does not look for an exact match. It checks whether the answer is correct, grounded in the source, and written in the right tone. You also look at the shared behavior of many sample inputs, not at one single answer.

The difference comes down to three points:

• The output is probabilistic, so the question changes from "is it equal?" to "is it acceptable?"
• Success is a mix of several dimensions, not one number.
• The test must rerun when the prompt, the model, or the data changes, not only when the code changes.

Evals therefore do not replace unit tests. They complete them. Therefore classic tests still check function calls, API connections, and format validation. Evals then measure the language quality on top of that.

What is silent degradation, and why do you need evals?

Silent degradation means answer quality drops without any error message. In practice, the system runs, the page loads, and an answer arrives. However, the answer may be vaguer, longer, or less accurate than before.

In practice, there are three typical triggers. The first is a small edit to the prompt. For instance, making one sentence politer can weaken another rule. The second is a model update from the provider, or your own switch to a different model. The third is new documents added to your knowledge base.

For example, think of a product description assistant for an online store. You shorten the prompt because you want to cut costs. The assistant keeps working, but it now skips the return conditions from time to time. The only way to notice is to measure with the same questions before and after the change.

So evals work less like an insurance policy and more like a compass. They do not prevent mistakes. Instead, they show early that your direction has changed. Check current model capabilities in the provider's official documentation, because behavior differences can change over time.

What is a test set (golden dataset), and how do you build one?

A test set is a list of inputs your application will really receive, each paired with a description of what a good answer looks like. In the field, this is also called a golden dataset. The word golden reflects that these examples serve as the trusted reference.

You can build a good test set with these steps:

1. Collect real user questions. Clean or anonymize records that contain personal data.
2. Group the questions: frequent ones, hard ones, ambiguous ones, and edge cases.
3. For each question, write the expected answer or the properties the answer must have.
4. Add out-of-scope questions too, because the application must be able to say "I don't know."
5. Name an owner for the set and review it on a regular schedule.

So the golden rule here is variety. A set with only easy examples looks great in every release and misleads you. Also, real typos, incomplete sentences, and irrelevant questions belong in the set.

You can also extend the set with synthetic data. Always review synthetic examples with human eyes, though. Our synthetic data guide covers that topic further.

Which criteria do you measure: accuracy, groundedness, tone, and format?

First, there is no single quality score. Every application cares about different dimensions, so you define the criteria first. Anthropic's official developer documentation also stresses that success criteria should be specific, measurable, and relevant to the purpose of the application.

The four most common criteria are:

• Accuracy: Is the answer factually correct, and does it really answer the question?
• Groundedness: Does the answer invent anything that is not in the documents you provided? This criterion is critical in RAG systems.
• Tone: Does the answer match your brand voice and the customer's expectation?
• Format: Is the answer in the requested structure, such as required fields, a length limit, or a list layout?

In addition, operational criteria such as consistency, latency, and cost can be added. Anthropic's documentation notes that most applications need several criteria at the same time.

Wrong information, however, is a topic of its own. We covered that risk in our AI hallucination guide. Here we only note this: a groundedness criterion is the most practical way to make hallucination measurable.

How does automated evaluation work?

Automated evaluation, in short, scores an answer through a rule or a program. A human does not read every answer, so you can run hundreds of examples in minutes. OpenAI's eval documentation describes the same approach with datasets, graders, and result analysis.

The main automated methods are:

• Exact match: If the answer is a category, such as "positive" or "negative", you compare it with the expected label.
• Rule checks: You check whether a required phrase appears, whether a banned phrase appears, and whether the length limit is exceeded.
• Semantic similarity: You measure whether the meaning of the answer is close to a reference answer, using embedding vectors.
• Model scoring: Another model scores the answer on a defined scale. We cover this method in its own section.

Rule-based methods are fast, cheap, and consistent, but they are limited. However, they cannot capture subjective qualities such as politeness or clarity. A good eval setup therefore combines simple rules with model scoring.

One principle matters here: run the cheapest check first. If the format is broken, scoring with a model makes little sense.

When is human evaluation essential?

Automated methods scale, but they do not fully replace human judgment. So human evaluation is essential in three situations.

The first is the moment a criterion is newly defined. If the team has not yet agreed on what counts as a "good" answer, people should first read a few hundred answers. This stage, then, sharpens the criterion.

The second is validating the judge model. If a model's scores do not match human scores, you cannot trust them. You have both a human and the judge score a small sample, then study the gap.

Third, there is high-risk output. For legal, financial, or safety-sensitive answers, a person should keep the last word.

Choosing which answers go to human review is also a design decision. A practical split looks like this: automated checks run on every change, while human review runs at intervals on a random sample. People should also read answers that the automated score called "good" but the customer complained about. Those answers are the most valuable new examples for your test set.

What is LLM-as-a-judge, and how does it work?

LLM-as-a-judge is an approach in which one language model scores the answer of another model or application, acting as the judge. It therefore offers a scalable option where human evaluation is slow and expensive.

Put simply, the logic is simple. You give the judge model three things: the question, the answer to evaluate, and a clear scoring rubric. Then the model returns a score or a category based on the rubric, often with a short reason.

There are three common usage styles:

• Single scoring: The answer gets a score on a scale, for example one to five.
• Binary classification: The answer is marked "follows the rule" or "does not follow the rule."
• Pairwise comparison: The judge picks which of two answers is better.

The paper "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena" by Zheng and colleagues is one of the core sources that studies this approach systematically. According to its abstract, strong judge models also can agree with human preferences at a high level. The same abstract also notes that judges have certain biases and limited reasoning ability. The next section covers exactly that.

How do you write a scoring rubric for a judge model?

The quality of a judge model depends heavily on the quality of the rubric. A vague instruction like "evaluate the answer" produces different scores on every run. Instead, describe in plain sentences what counts as good.

A good rubric has these parts:

• Task definition: State in one sentence what the judge evaluates.
• Criterion description: For groundedness, for example, write "every claim in the answer must appear in the provided document."
• Scale definition: Explain what each score means with short examples.
• Reason request: Asking for a short reason first and the score second improves consistency.
• Output format: Define how the score is returned so a program can read it.

After that, try the rubric on a small set of examples. Read the judge's scores yourself and feed any mismatch with your expectation back into the rubric. That way the rubric becomes a versioned, improvable asset, just like code.

Which biases does a judge model have?

A judge model is not a neutral measuring device. The paper by Zheng and colleagues names three main biases in its abstract: position bias, verbosity bias, and self-enhancement bias. It also lists limited reasoning ability as a constraint.

Here is each one in simple terms:

• Position bias: When you show two answers side by side, the judge may prefer the one in a certain position without weighing the content.
• Verbosity bias: The judge may score a longer, more detailed-looking answer higher, even when it is not actually better.
• Self-enhancement bias: A judge model may score answers that resemble its own output higher.
• Limited reasoning: Answers that need math or multi-step logic can be misjudged by the judge as well.

However, these biases do not invalidate the judge approach. They do show that you should not read the scores as absolute truth. In other words, a judge score is a signal, not a verdict.

The ideas from our AI bias guide also apply here. The judge is a model too, and it carries tendencies from its training data.

How do you reduce judge model bias?

You cannot remove bias completely, but you can shrink its effect. The following steps are the core defenses that work in practice.

1. In pairwise comparison, swap the order of the answers and score twice. If the result flips with the order, then treat that example as uncertain.
2. State in the rubric that length is not a reason for a higher score. Add a rule such as "a shorter, correct answer beats a long, scattered one."
3. Use a different model as judge than the one that produced the answer. Anthropic's documentation also recommends choosing a different model for grading than the one used in production.
4. Keep the scoring scale narrow. Clear categories like pass and fail are more consistent than fine-grained differences.
5. Ask the judge to write a short reason before it gives a score.
6. Have humans score a small sample regularly and compare it with the judge.

The sixth step matters most, because it lets you validate the judge against an independent reference. Do not use a judge whose scores disagree with human scores until you revise its rubric.

How does regression testing work with evals?

A regression test checks whether a change broke something that worked before. Therefore the logic in evals is the same. The only difference is that what you measure is language quality, because the output is text.

The process runs like this. First you run the current version on the test set and keep the result as a baseline. Then you change the prompt, the model, or the knowledge base. After that you rerun the same set and place the results side by side.

When you compare, look at these questions:

• Which criterion dropped, and which one improved?
• Is the drop concentrated in a particular group of questions?
• When you inspect single examples, do the failures share a pattern?

Still, a change can improve one criterion while it breaks another. For example, the tone becomes warmer, but groundedness drops. You only see this balance by measuring.

When you set a threshold, first agree as a team on the "lowest acceptable level." Set current threshold values by your own risk tolerance, because no universal number exists. For judging whether differences between two variants are meaningful, you can use our A/B test calculator as a conceptual reference.

What are LLM evals, and are they the same as benchmarks?

No, they are not the same, because their scope differs. A benchmark is a public, standard collection of tests designed to compare models on general abilities. LLM evals, on the other hand, measure your own application with your own data and your own criteria.

Think of a benchmark, for example, as a general driving license exam. Evals are the in-house training that measures how safely that driver handles your delivery route in your own city. The license is a good start, but it still does not mean the driver knows the route.

There are three core differences:

• Scope: A benchmark measures general tasks, while an eval measures your task.
• Ownership: A benchmark is public, while your eval set is private to you and can stay confidential.
• Purpose: A benchmark informs model choice, while an eval tracks the quality of your application over time.

Benchmark scores can be a useful first filter when you pick a model. However, scores age fast and may not represent your use case. Check current results on the provider's official page, but make the final call with your own eval set.

How do evals differ from similar terms?

The field grows fast, so close terms get mixed up. The table below helps you separate evals from neighboring concepts.

For red teaming, see our AI red teaming guide. For guardrails, see our guardrails guide. These two work as separate layers next to an eval setup.

How do evals differ for RAG and agent applications?

In a simple chat assistant, you only measure the answer, so evaluation is easy. In a RAG-based system, however, there are two separate stages: finding the right document and writing an answer from it. You therefore need to evaluate both stages on their own. Because of that, if retrieval is poor, even the best language model cannot answer well. We explained RAG in our RAG guide.

Two typical questions for RAG are:

• Are the retrieved passages really relevant to the question?
• Does the generated answer use only the information in those passages?

In agent applications, what you measure is not one answer but a series of decisions. Which tool the agent picks, whether it calls the tool with the right parameters, and whether it finishes in a reasonable number of steps are separate criteria. Your test set can therefore hold the expected sequence of steps as well as questions and answers.

In both cases, the principle stays the same. Measure the components separately first, then look at the end-to-end behavior. That way, when an error appears, you can quickly tell whether the problem sits in retrieval, in the context, or in the answer writing.

How do offline and online evaluation work together?

Offline evaluation means measuring with a ready test set before release. Online evaluation means sampling real user traffic and scoring it with the same criteria. The two do not compete. They complete each other.

Offline measurement is fast and safe. You test a prompt change before it reaches users. However, a test set never represents the real world perfectly. Online sampling reveals question types you did not expect.

A practical loop looks like this:

1. Validate the change with the offline set.
2. Roll the change out gradually.
3. Pick random samples from live traffic and score them with the same criteria.
4. Review low-scoring answers and answers that came with user complaints.
5. Add the interesting cases to the offline test set.

Because of this loop, your test set a little more realistic every week. When you handle user data in online sampling, follow privacy rules and mask the data if needed.

Example scenario: how do you evaluate a customer support assistant?

This is an example scenario, not a real customer result. Suppose an online store has a support assistant that answers return and shipping questions. The assistant writes its answers from the store's policy documents.

First you build the test set. You take questions from real support records and remove personal data. For each question you pair the relevant section of the policy document as a reference. You also add out-of-scope questions, such as the weather or another brand's policy.

Then you choose the criteria:

• Groundedness: Does the answer use only information from the policy document?
• Accuracy: Do the return period and conditions match the document?
• Tone: Does it write to the customer in a respectful and clear way?
• Format: Does it give steps as a numbered list when needed?

Next you assign rule checks (format, required phrases) to code and subjective criteria (tone, groundedness) to a judge model. You rerun the set for every new prompt version. If you see a drop, you check which question group broke. This turns the "I think it got better" debate into a decision based on measurement.

Which settings affect eval results?

When you run the same prompt twice, you can get two different answers. The main reason is the randomness settings the model uses while it generates. When you read eval results, you therefore need to keep the settings fixed.

As temperature rises, answers become more varied. For an eval, this means the same example can pass in one run and fail in another. Our temperature and top-p guide explains these settings in detail.

Practical recommendations:

• Fix and record the settings in every eval run.
• For tasks where variety matters, run each example several times and look at the spread of results.
• Store the prompt, the model name, and the settings together with the result. That way you can reproduce an old result.

What you place in the context also changes the outcome. We covered that in our context engineering guide. An eval makes the context design measurable too.

Which mistakes break an eval process most often?

Eval setups usually break because of habits, not technical causes. The most common problems we see in the field look like this.

• Looking at a single average score. An average can hide a serious collapse in one group of questions. Slice the results by group.
• Leaving the set without an owner. If nobody updates it, the set ages and drifts away from real usage.
• Never testing the judge rubric. A vague rubric produces inconsistent scores.
• Not recording results. If you do not know which prompt earned which score, you cannot compare back in time.
• Inflating the number of criteria. Too many criteria turn signal into noise. Three to five well-chosen criteria are often enough.

Process solves all of these mistakes. Treat the set like a product: give it an owner, version it, and review it regularly.

What are the risks and limits of evals?

Evals are a powerful tool, but they have limits. Knowing them prevents false confidence.

The main limits are:

• The test set can be biased. It only holds the questions you have seen before, and it can miss new kinds that arrive tomorrow.
• Overfitting is a risk. If you reuse the same set and tune the prompt to it, the application looks good on the set and average in real life. Keep a separate validation set for that reason.
• Judge scores are imperfect. As the bias sections show, scores need human validation.
• Cost and time add up. Running large sets with a judge model on every change costs money. Use a fast core set and a wider periodic set.
• A wrong criterion gives a wrong result. Optimizing a badly defined criterion perfectly means hitting the wrong target.

Evals do not offer a guarantee. They do not show that errors are absent. They show that known risks are under control. When you add records with personal data to a test set, respect data protection law. This article is not legal advice.

What are LLM evals for a small business, and what does a practical checklist look like?

For a small team, the answer to what are LLM evals is not to build a perfect platform. It is to start a small, regular habit. The list below gives a starting frame.

1. Write the one-sentence purpose of the application and keep the success criteria between three and five items.
2. Build a core test set from real inputs, and add edge cases and out-of-scope questions.
3. Pick a scoring method for each criterion: rule, semantic similarity, judge model, or human.
4. If you use a judge model, write the rubric, test it by swapping the order, and validate it with a human sample.
5. Save the prompt, the model, and the settings as a version.
6. Run the set before and after every change and compare the results.
7. Add faulty live answers back to the test set.
8. Decide how often the set and the criteria get reviewed.

Once you set this list up, switching to new models stops being scary. You answer the question "is the new model good for our work?" with measurement instead of opinion. Check current model features and terms in the provider's official documentation.

How can our team help with this process?

At Talha Aslan and team, we care about measuring AI applications, not only building them. The quality of a live assistant does not stay at its launch-day level. It changes with every prompt and model change.

Our team helps with designing a test set for your business, defining criteria, and connecting the evaluation routine to your existing workflow. When we work with your real data, we clarify privacy and data limits from the start. We do not promise results. We aim to build a measurable, repeatable, and explainable process.

If you want to scope your AI project, you can look at our AI consulting page. As sources, we recommend OpenAI's evals guide, Anthropic's guide to developing tests, and the judge model paper by Zheng and colleagues.

Finally, this article is for general information. Model features and tool interfaces can change, so check the official documents before you apply anything. For the prompt design side, you can also read our prompt engineering guide.