---
title: "Meet the Evaluations framework"
description: "Evaluate probabilistic AI features with Swift Testing, quantitative metrics, robust datasets, and model judges in the Evaluations framework."
shoutout: "Unofficial, made with love by Superwall - the best way to monetize your apps."
category: "AI & Machine Learning"
---

# Meet the Evaluations framework


[Watch on Apple Developer](https://developer.apple.com/videos/play/wwdc2026/298/)

Evaluate probabilistic AI features with Swift Testing, quantitative metrics, robust datasets, and model judges in the Evaluations framework.

## TL;DR

- The Evaluations framework lets you test stochastic model-driven features by measuring behavior across datasets instead of relying only on deterministic unit tests.
- An Evaluation defines the subject under test, a dataset of ModelSample values, Metrics and Evaluators, aggregate statistics, and a Swift Testing test with an optimization target.
- Robust datasets should cover varied inputs and expected outputs; SampleGenerator can synthesize more samples from seed ModelSamples.
- ModelJudgeEvaluator uses a capable language model, score dimensions, rationales, and ModelJudgePrompt context to measure qualitative traits that code heuristics cannot capture.

## Why evaluations are needed

Generative AI features break the usual testing assumption that the same input always produces the same output. Unit tests still matter, but they are insufficient for verifying probabilistic behavior such as unexpected outputs, unsafe responses, or agent paths that vary across runs.

The Evaluations framework provides protocols and types for measuring intelligent features at scale. The session focuses on language-model-backed features, but the framework can also evaluate other stochastic systems such as classifiers and linear regression models.

- Use evaluations to express expectations and measure how often a feature meets them.
- Use automated metrics and aggregate statistics instead of relying on one-off manual inspection.
- Use evaluation results to drive a hill-climbing loop: inspect failures, change the feature or prompt, rerun, and compare.

## Build an evaluation

The Book Tracker example evaluates a BookTaggingService that generates tags from a user's book review. A first manual pass identifies expectations: tag count should be in range, tags should not include titles, tags should be single-word or hyphenated, tags should include useful browsing categories, and tags should reflect the book rather than only the reader's opinion.

An evaluation has five parts: define the subject under test, define the dataset, define metrics and evaluators, aggregate measurements across samples, and run the evaluation from a test target.

- Implement the Evaluation protocol and import Evaluations.
- Use subject(from:) to call the feature being measured and return its output.
- Use ModelSample to pair prompts with expected values.
- Use Metric plus Evaluator for per-sample measurements such as pass/fail tag-count checks.

### Skeleton evaluation and tag-count evaluator

Defines a quantitative metric and an evaluator that records whether each generated tag list has the expected size.

```swift
import Evaluations

struct BookTaggingEvaluation: Evaluation {
    let tagCount = Metric("TagCount")

    var evaluators: Evaluators {
        Evaluator { _, subject in
            let count = subject.value.tags.count
            if count >= 3 && count <= 8 {
                return tagCount.passing(rationale: "\(count) tags")
            }
            return tagCount.failing(rationale: "Got \(count) tags, expected 3-8")
        }
    }
}
```

### Dataset with ModelSample

Wraps representative inputs and ideal outputs so the same evaluation can run repeatedly over known samples.

```swift
var dataset = ArrayLoader(samples: [
    ModelSample(
        prompt: "okay I am OBSESSED and I need everyone to read this RIGHT NOW...",
        expected: BookTags(tags: ["classic", "romance", "wit", "regency"])
    ),
    ModelSample(
        prompt: "Read this in one sitting between midnight and 4am and I cannot...",
        expected: BookTags(tags: ["classic", "gothic", "horror", "vampire", "suspense"])
    )
])
```

## Run with Swift Testing and inspect reports

Evaluations integrate with Swift Testing. A test uses the .evaluates trait to run an evaluation and can assert on aggregate values from the evaluation results bundle. In the example, the optimization target requires the tag-count metric to pass at least 80% of the time.

Xcode's evaluation test report shows aggregate metrics, per-sample rows, prompts, measurements, rationales, and the full model response. This is used to diagnose failures and decide what change to try next.

- Use aggregateValue(.mean(of: metric)) to assert an optimization target.
- Attach notes to an evaluation run so different configurations can be compared later.
- A failing optimization target is treated as signal for analysis, not just a binary test failure.

### Swift Testing optimization target

Runs the evaluation from a test target and fails the test if the mean pass rate for tag count falls below the chosen target.

```swift
@Test("Book Tag Evaluations", .evaluates(evaluation, info: evaluationInfo))
func evaluateBookTagging() async throws {
    let result = EvaluationContext.current.result
    let rangeMetric = BookTagEvaluationTests.evaluation.tagCount
    #expect(result.aggregateValue(.mean(of: rangeMetric)) >= 0.8)
}
```

### Constrain generated output with @Guide

The example hill-climbing change adds a count range to the generated BookTags shape, then reruns the evaluation to confirm the effect.

```swift
@Generable
struct BookTags: Codable {
    @Guide(
        description: "Descriptive tags capturing themes, genres, moods, and topics from the summary",
        .count(3...8)
    )
    var tags: [String]
}
```

## Scale datasets and refine quantitative metrics

Two samples are enough to prove the workflow, but not enough to characterize an intelligent feature. The session recommends datasets with broad variety: genres, review lengths, fiction and non-fiction, forms such as novels or essays, and personal opinions that the feature should ignore.

SampleGenerator can synthesize more samples from seed ModelSamples when manual data creation does not scale. After expanding the Book Tracker dataset, the evaluation revealed that a guide change made the service always generate eight tags, which led to more detailed metrics.

- Use scoring metrics when the measurement is not just pass/fail, such as recording total tag count.
- Aggregate means, standard deviations, and variances to identify distribution problems.
- Add rule-based evaluators for quantifiable expectations, such as no spaces in tags or at least one tag from a known genre list.

### Aggregate range and distribution metrics

Combines pass-rate and distribution statistics to detect behavior such as always returning the maximum number of tags.

```swift
let tagCount = Metric("TagCount")
let tagTotal = Metric("TagTotal")

func aggregateMetrics(using aggregator: inout MetricsAggregator) {
    aggregator.computeMean(of: tagCount)
    aggregator.group("Distribution of Tag Totals") { aggregator in
        aggregator.computeStandardDeviation(of: tagTotal)
        aggregator.computeMean(of: tagTotal)
        aggregator.computeVariance(of: tagTotal)
    }
}
```

### Rule-based evaluators for additional expectations

Shows how simple heuristics can measure traits that are easy to express in code.

```swift
let wordCount = Metric("WordCount")
Evaluator { _, subject in
    for tag in subject.value.tags {
        if tag.contains(" ") {
            return wordCount.failing(rationale: "Tag \(tag) contains multiple words")
        }
    }
    return wordCount.passing()
}

let hasGenreTag = Metric("HasGenreTag")
Evaluator { _, subject in
    let tags = subject.value.tags.map { $0.lowercased() }
    let knownGenres = await BookTaggingService.knownGenres
    for tag in tags where knownGenres.contains(tag) {
        return hasGenreTag.passing(rationale: "Matched \(tag)")
    }
    return hasGenreTag.failing()
}
```

## Use model judges for qualitative metrics

Some failures are qualitative: tags can satisfy count, word-count, and genre checks while still being unhelpful, irrelevant, or based on the reader's opinion instead of the book. A model judge uses a second language model to score feature output in a way similar to a human reviewer, consistently across the dataset.

The judge should be at least as capable as the model being evaluated. In the example, the app feature uses an on-device model, while the judge uses PrivateCloudComputeLanguageModel. ModelJudgeEvaluator is another Evaluator, so qualitative and quantitative metrics can coexist in the same evaluation.

- Define an explicit scale; the example uses four numeric levels to avoid a neutral middle score.
- Read rationales, because they explain why the judge scored a sample the way it did.
- If the judge's score does not match human judgment, refine the scoring guide rather than assuming the judge is wrong.

### Simple model judge

Adds a qualitative TagQuality metric scored by a more capable model judge.

```text
ModelJudgeEvaluator(
    "TagQuality",
    scale: .numeric([
        4: "Tags are relevant and helpful for browsing",
        3: "Mostly relevant, one tag too vague or generic",
        2: "Several tags are wrong or generic",
        1: "Unhelpful or irrelevant"
    ]),
    judge: PrivateCloudComputeLanguageModel()
)
```

## Refine judges with dimensions and app context

A broad model-judge question can hide disagreement. The session splits tag quality into ScoreDimensions such as Relevance and Usefulness, each with its own description and scale. Separate dimensions make rationales more diagnostic: one can explain what kind of tag is wrong, while another explains why it fails for browsing.

ModelJudgePrompt supplies application context, formats the evaluation target, and can pass reference information such as expected tags. In Book Tracker, the prompt tells the judge that the tags are for a personal library browsing and filtering experience, not a review platform.

- If all scores are the same, the judge question is probably too broad.
- If the problem cannot be isolated, split the scoring into dimensions.
- If the judge misunderstands the product intent, add context with ModelJudgePrompt.

### Score dimensions and contextual judge prompt

Breaks a qualitative judge into separate dimensions and gives the judge product-specific context.

```swift
ScoreDimension(
    "Relevance",
    description: """
    Whether each tag describes a quality, theme, or tone of the book itself rather than incidental details or the reader's personal reactions.
    """,
    scale: .numeric([
        4: "Every tag describes the book itself",
        3: "Most tags describe the book",
        2: "Some tags describe personal reactions",
        1: "Tags don't meaningfully describe the book"
    ])
)

ModelJudgeEvaluator(
    judge: PrivateCloudComputeLanguageModel(),
    dimensions: [relevance, usefulness],
    prompt: ModelJudgePrompt(
        instructions: """
        You are evaluating tags generated for a personal book-tracking app where users organize their library by browsing and filtering tags.
        """,
        evaluationTarget: { value in
            "\(value.tags.count) Generated tags: " + value.tags.joined(separator: ", ")
        },
        reference: { input, _ in
            let expectedTags = input.expected?.tags.joined(separator: ", ")
            return ["Expected Tags": expectedTags ?? "No expected tags defined"]
        }
    )
)
```

## Resources

- [Book Tracker: Using Evaluations to evaluate an intelligent feature](https://developer.apple.com/documentation/Evaluations/book-tracker-using-evaluations-to-evaluate-an-intelligent-feature)
- [Designing datasets to test your feature](https://developer.apple.com/documentation/Evaluations/designing-evaluation-datasets)
- [Designing effective evaluations](https://developer.apple.com/documentation/Evaluations/designing-effective-evaluations)
- [Evaluating language model responses](https://developer.apple.com/documentation/Evaluations/evaluating-language-model-responses)

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