Write Eval Specs

Spec File Anatomy

Eval specs use the evaluate Playwright fixture (not test). A spec file follows this structure:

import { evaluate, tags, selectEvaluators, type Example, type TaskOutput } from '@kbn/evals';

evaluate.describe('Suite name', { tag: tags.serverless.observability.complete }, () => {
  evaluate.beforeAll(async ({ fetch, log }) => {
    // one-time setup: install docs, create agents, load archives
  });

  evaluate.afterAll(async ({ fetch, log }) => {
    // teardown: uninstall docs, delete agents, unload archives
  });

  evaluate('test name', async ({ executorClient, connector }) => {
    await executorClient.runExperiment(
      { datasets: [dataset], task },
      evaluators
    );
  });
});

When a suite has a custom src/evaluate.ts, import from there instead of @kbn/evals:

import { evaluate } from '../src/evaluate';

Tags

Every evaluate.describe must have a tag. Common choices:

Import tags from @kbn/scout or @kbn/evals (re-exported).

Datasets

A dataset is an array of examples with typed input, output (expected), and optional metadata:

type MyExample = Example<
  { question: string },
  { expectedAnswer: string },
  { tags?: string[] }
>;

const dataset = {
  name: 'my-dataset',
  description: 'What this dataset tests',
  examples: [
    {
      input: { question: 'What is 2+2?' },
      output: { expectedAnswer: '4' },
      metadata: { tags: ['math'] },
    },
  ],
};

Keep datasets focused. For local iteration, use --grep to run a subset:

node scripts/evals start --grep "my test name"

Tasks

The task function receives an example and returns the output to evaluate:

task: async ({ input }) => {
  const result = await someKibanaApi(input.question);
  return { answer: result.content };
}

Tasks can use any fixture available in the evaluate callback: fetch, inferenceClient, connector, esClient, kbnClient, or custom fixtures like chatClient.

Evaluators

There are two ways to provide evaluators to runExperiment:

1. Inline array -- pass evaluator objects directly (simple suites)
2. selectEvaluators -- typed wrapper that enforces Example/TaskOutput generics

CODE Evaluators

Deterministic, no LLM call. Use for binary checks:

{
  name: 'NonEmpty',
  kind: 'CODE',
  evaluate: async ({ output }) => ({
    score: output?.documents?.length > 0 ? 1 : 0,
  }),
}

LLM-as-Judge Criteria

Use evaluators.criteria(criteriaArray) for subjective quality checks. The judge LLM scores each criterion:

evaluators.criteria([
  'The response correctly identifies the top users.',
  'The response includes risk scores.',
]).evaluate({ input, output, expected, metadata })

Correctness Analysis

Compares output against expected answer:

evaluators.correctnessAnalysis().evaluate({ input, output, expected, metadata })

Groundedness Analysis

Checks if output is grounded in provided context:

evaluators.groundednessAnalysis().evaluate({ input, output, expected, metadata })

Trace-Based Evaluators

Available from evaluators.traceBasedEvaluators:

• inputTokens, outputTokens, cachedTokens -- token usage
• toolCalls -- number of tool calls
• latency -- span latency in seconds

These read from the tracing ES cluster and require EDOT to be running.

RAG Evaluators

For retrieval-augmented generation with ground truth:

import { createPrecisionAtKEvaluator, createRecallAtKEvaluator, createF1AtKEvaluator } from '@kbn/evals';

See [evaluator-patterns.md](references/evaluator-patterns.md) for full examples.

Available Fixtures

The evaluateDataset Pattern

For suites with many specs that share the same task + evaluator wiring, extract a reusable helper:

src/evaluate_dataset.ts:

import type { DefaultEvaluators, EvalsExecutorClient } from '@kbn/evals';
import type { MyChatClient } from './chat_client';

export type EvaluateDataset = (opts: {
  dataset: { name: string; description: string; examples: MyExample[] };
}) => Promise<void>;

export function createEvaluateDataset({
  chatClient, evaluators, executorClient,
}: {
  chatClient: MyChatClient;
  evaluators: DefaultEvaluators;
  executorClient: EvalsExecutorClient;
}): EvaluateDataset {
  return async ({ dataset }) => {
    await executorClient.runExperiment(
      {
        datasets: [dataset],
        task: async ({ input }) => {
          const response = await chatClient.converse({ messages: [{ message: input.question }] });
          return { messages: response.messages, steps: response.steps };
        },
      },
      [myCriteriaEvaluator, myToolCallsEvaluator]
    );
  };
}

In the spec:

import { evaluate as base } from '../src/evaluate';
import type { EvaluateDataset } from '../src/evaluate_dataset';
import { createEvaluateDataset } from '../src/evaluate_dataset';

const evaluate = base.extend<{ evaluateDataset: EvaluateDataset }, {}>({
  evaluateDataset: [
    ({ chatClient, evaluators, executorClient }, use) => {
      use(createEvaluateDataset({ chatClient, evaluators, executorClient }));
    },
    { scope: 'test' },
  ],
});

evaluate.describe('My suite', { tag: tags.serverless.search }, () => {
  evaluate('my test', async ({ evaluateDataset }) => {
    await evaluateDataset({ dataset: { name: '...', description: '...', examples: [...] } });
  });
});

Setup and Teardown

Use evaluate.beforeAll / evaluate.afterAll for expensive one-time operations:

• Install product docs: POST to /internal/productdocbase/install
• Create agents/rules: Use fetch or kbnClient
• Load ES archives: Use esArchiver.load(archivePath) (requires custom fixture)

Always clean up in afterAll -- delete agents, uninstall docs, unload archives.

Running Locally

# Full interactive flow
node scripts/evals start

# Specify model and judge
node scripts/evals start --model <connector-id> --judge <connector-id>

# Filter to a specific test
node scripts/evals start --grep "my test name"

# Run directly (services already running)
node scripts/evals run --model <connector-id> --judge <connector-id>

Common Mistakes

• Forgetting the tag on evaluate.describe -- Scout validates tags at runtime.
• Missing afterAll cleanup -- leftover agents/docs pollute subsequent runs.
• Overly large datasets for local iteration -- use --grep to target a single evaluate() block.
• Importing evaluate from @kbn/evals when the suite has a custom src/evaluate.ts -- you'll miss custom fixtures.
• Using test instead of evaluate -- the evaluate fixture provides all the evals-specific wiring.

References

• Evaluator type examples with real code: [references/evaluator-patterns.md](references/evaluator-patterns.md)
• Suite scaffolding: use the evals-create-suite skill