Evaluating AI Systems: The Fundamentals

Evaluating a model means measuring how well it performs on the tasks that matter to your business.

That could mean checking whether a customer support model gives accurate answers, whether a coding agent completes tasks correctly, or whether a financial assistant follows your internal policies. The goal is to turn a broad question like “Is this model good enough?” into a set of specific, measurable tests.

A useful evaluation usually includes representative examples, clear criteria for success, and a consistent way to score outputs. The result should help you compare models, decide whether a system is ready to deploy, and identify where it still fails.

Evaluations are most useful when they reflect real workflows rather than generic capabilities.

Start by defining the work you expect the system to do.

From there, collect a set of realistic tasks or inputs, define what a good response should look like, and choose how each response will be scored. Depending on the task, scoring might be automated, performed by another model, reviewed by people, or handled by domain experts.

A typical process looks like this:

1. Define the tasks that matter.
2. Build a representative set of examples.
3. Define success criteria.
4. Choose the right grading method.
5. Run one or more models.
6. Review scores and failure patterns.
7. Update the evaluation as new issues appear.

The evaluation should reflect the way the system will actually be used. A model that performs well on a public benchmark may still perform poorly on your business’ specific workflows, data, tools, or policies.

A good evaluation gives you information you can act on.

It should test realistic tasks, measure the aspects of quality that matter to your business, and produce results that are reliable enough to support decisions. It should also include enough difficult and edge-case examples to expose meaningful differences between models.

Strong evaluations usually have:

• Representative tasks drawn from real or realistic workflows
• Clear success criteria
• Grading methods that are appropriate for the task
• Consistent evaluator behavior
• Enough examples to support useful conclusions
• Coverage of important failure modes
• Ongoing updates as products and models change

The most useful evaluations also make it easy to understand why a model failed. A single average score can tell you which model performed better. Detailed failure analysis tells you what to improve.

An eval set is the collection of tasks or examples used to measure model performance.

For a customer support system, an eval set might contain real support questions and expected outcomes. For a legal assistant, it could include research, drafting, and document review tasks. For an agent, it might contain multi-step workflows that require using tools or navigating software.

A good eval set should reflect the range of work the system will encounter in practice. That includes common tasks, difficult cases, edge cases, and situations where mistakes would be especially costly.

Eval sets can be created from historical data, production traffic, expert-designed scenarios, or a combination of sources. They should also evolve over time as you discover new failure modes or expand the system into new workflows. We can help you build these eval sets to match the user personas and use-case distributions that you’d like to target.

If you’re not sure where to start, our off-the-shelf (OTS) catalog includes ready-to-use eval sets spanning professional domains like coding, finance, legal, and healthcare, as well as capabilities like multimodal understanding, instruction following, document reasoning, and compliance.

A rubric is a structured way to define what a good model response should look like. The term is used in a few different ways in AI evaluation, so it helps to be explicit about what the rubric contains.

In human or preference-based evaluation, a rubric often refers to the dimensions that evaluators should consider when judging a response. These might include accuracy, completeness, clarity, instruction following, tone, or use of evidence. The rubric gives evaluators a shared framework for making consistent judgments.

In automated evaluation, “rubric” increasingly refers to a collection of specific, atomic criteria that describe the components an ideal response should contain. For example, a rubric for a financial analysis task might include separate criteria for calculating a metric correctly, identifying a particular risk, using the required assumptions, and reaching the right conclusion. Each criterion can be checked independently by an autograder.

Both approaches serve the same broader purpose: making the definition of quality explicit. The right level of detail depends on how the evaluation will be scored. Preference evaluations often benefit from broader dimensions, while autograded tasks typically need more precise criteria that can be evaluated consistently.

A grader is the method used to score a model response.

A grader can be a rule, a software check, another AI model, a human evaluator, or a domain expert. The right choice depends on what you are trying to measure.

If there is one exact correct answer, an automated check may be enough. If the task involves judgment, ambiguity, or specialized knowledge, human review may be more appropriate. In many enterprise evaluations, teams use a combination of grading methods.

The grader matters because the evaluation can only be as reliable as the scoring process behind it. Before relying on an automated or model-based grader at scale, it is useful to compare its decisions against trusted human judgments.

A loss is a way in which the model's output falls short of the desired result. A failure mode is a recurring pattern behind those losses.

For example, a model might:

• Give the wrong answer
• Miss an important part of the task
• Ignore instructions
• Use the wrong tool
• Make unsupported claims
• Produce an answer that is technically correct but unusable
• Fail under a specific type of edge case

Looking at failure modes is often more useful than looking only at an overall score. Two models with similar average performance may fail in very different ways.

For enterprise systems, the severity of the failure also matters. A formatting mistake and an incorrect compliance recommendation should not be treated as equally important.

Tracking failure modes helps teams decide what to fix, what to monitor, and what new examples should be added to future evaluations.

There is no single number that works for every evaluation.

The right size depends on how many kinds of tasks you need to cover, how much variation exists within those tasks, and how precise your decisions need to be.

A small, carefully designed set can be useful early on for finding obvious problems and comparing major differences between models. Larger sets become more important when you need stable measurements, want to detect smaller performance changes, or need confidence across many different workflows.

Coverage is often more important than raw size. A thousand nearly identical examples may tell you less than a smaller set that includes the important task types, edge cases, and failure modes.

For enterprise evaluations, it is usually better to start with a representative set and expand it based on what you learn.

A reliable evaluation should produce similar conclusions when it is repeated under similar conditions.

That requires consistency in both the tasks being tested and the way responses are scored. If different evaluators regularly disagree, or if small changes in the eval set produce very different rankings, the results may not be stable enough to support important decisions.

Teams can improve reliability by:

• Writing clear rubrics
• Calibrating evaluators on shared examples
• Measuring agreement between graders
• Reviewing ambiguous cases
• Comparing automated graders against human judgments
• Testing results across multiple samples
• Looking at confidence intervals or other measures of uncertainty

Reliability becomes especially important when the evaluation is being used to choose a model, approve a launch, or measure whether a training intervention worked.

Evaluations should change as the system changes.

New models may fail in different ways. Products may add new workflows. Users may find edge cases that were never included in the original test set. Internal policies and quality standards can also change over time.

A strong evaluation process captures those changes and feeds them back into the eval set.

Many teams maintain a stable core set for tracking performance over time, then add new examples as important failures appear in testing or production. This allows them to preserve comparability while keeping the evaluation relevant.

For systems that are actively being developed or used in production, evaluation is usually an ongoing process rather than a one-time test.