/testing-llm

name: testing-llm
license: MIT
compatibility: "Claude Code 2.1.277+."
description: "LLM testing patterns: mocked responses, DeepEval and RAGAS evaluation, structured output checks, agentic tests. Use when testing AI features."
user-invocable: false
disable-model-invocation: false
targets:
  - library: "deepeval"
    version: ">=4.2.7"
  - library: "ragas"
    version: ">=0.4.0"
metadata:
  owner-agent: "test-generator"
  category: document-asset-creation
  version: "2.1.0"
  author: "OrchestKit"
  complexity: "medium"
  tags: "testing, llm, ai, deepeval, ragas, evaluation, mocking"
allowed-tools: "Read Glob Grep WebFetch WebSearch"

LLM & AI Testing Patterns

Patterns and tools for testing LLM integrations, evaluating AI output quality, mocking responses for deterministic CI, and applying agentic test workflows (planner, generator, healer). Of that trio only the healer keeps a local reference here; the planner and generator stages belong to the `testing-e2e` skill.

Quick Reference

| Area | File | Purpose | |------|------|---------| | **Rules** | `rules/llm-evaluation.md` | DeepEval quality metrics, Pydantic schema validation, timeout testing | | **Rules** | `rules/llm-mocking.md` | Mock LLM responses, VCR.py recording, custom request matchers | | **Reference** | `references/ork-delta.md` | House rules the vendor docs do not carry: GEval and RAGAS API corrections, threshold direction, cassette path, golden-dataset and latency budgets | | **Reference** | `references/healer-agent.md` | Auto-fixes failing tests (selectors, waits, dynamic content) | | **Reference** | `references/langfuse-v4.md` | Langfuse Python SDK v4 tracing and dataset runs, plus the non-zero throughput assertion | | **Checklist** | `checklists/llm-test-checklist.md` | Complete LLM testing checklist (setup, coverage, CI/CD) |

Upstream coverage (do not restate)

DeepEval, RAGAS, VCR.py and Playwright document themselves. This skill carries only the OrchestKit delta (`references/ork-delta.md`) plus the house subsets in `rules/` and `checklists/`. Fetch the source below instead of expecting the material here.

| Topic | Source | |-------|--------| | Full DeepEval metric catalog and per-metric constructor arguments (the house threshold table and the two-metric quick start stay in this file, `rules/llm-evaluation.md` and `checklists/llm-test-checklist.md`) | https://deepeval.com/docs/metrics-introduction | | `GEval` custom criteria: `evaluation_params`, `evaluation_steps`, `criteria` (the house import correction stays in `references/ork-delta.md`) | https://deepeval.com/docs/metrics-llm-evals | | `HallucinationMetric` arguments (the house 0.3 ceiling and the inverted-direction warning stay in `references/ork-delta.md`) | https://deepeval.com/docs/metrics-hallucination | | RAGAS metric catalog (`Faithfulness`, `LLMContextRecall`, `FactualCorrectness`) | https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/ | | `EvaluationDataset` construction (the house note on the post-0.2 field names stays in `references/ork-delta.md`) | https://docs.ragas.io/en/stable/concepts/components/eval_dataset/ | | VCR.py configuration keys (the house record-mode gate and header filters stay in `rules/llm-mocking.md`) | https://vcrpy.readthedocs.io/en/latest/configuration.html | | Playwright Planner and Generator agents, `init-agents` CLI and generated files (the house healer subset stays in `references/healer-agent.md`) | https://playwright.dev/docs/test-agents | | Playwright semantic locator ladder used by generated tests | `testing-e2e` skill (`rules/e2e-playwright.md`) plus https://playwright.dev/docs/locators | | Confidence intervals over metric score samples | https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html |

When to Use This Skill

• Testing code that calls LLM APIs (OpenAI, Anthropic, etc.)
• Validating RAG pipeline output quality
• Setting up deterministic LLM tests in CI
• Building evaluation pipelines with quality gates
• Applying agentic test patterns (plan -> generate -> heal)

LLM Mock Quick Start

Mock LLM responses for fast, deterministic unit tests:

from unittest.mock import AsyncMock, patch
import pytest

@pytest.fixture
def mock_llm():
    mock = AsyncMock()
    mock.return_value = {"content": "Mocked response", "confidence": 0.85}
    return mock

@pytest.mark.asyncio
async def test_with_mocked_llm(mock_llm):
    with patch("app.core.model_factory.get_model", return_value=mock_llm):
        result = await synthesize_findings(sample_findings)
    assert result["summary"] is not None

**Key rule:** NEVER call live LLM APIs in CI. Use mocks for unit tests, VCR.py for integration tests.

DeepEval Quality Quick Start

Validate LLM output quality with multi-dimensional metrics:

from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric, FaithfulnessMetric

test_case = LLMTestCase(
    input="What is the capital of France?",
    actual_output="The capital of France is Paris.",
    retrieval_context=["Paris is the capital of France."],
)

assert_test(test_case, [
    AnswerRelevancyMetric(threshold=0.7),
    FaithfulnessMetric(threshold=0.8),
])

Library notes (DeepEval, RAGAS)

**DeepEval** metrics expose a `reason` field alongside the numeric score when `include_reason=True`, so a failing CI build gets a human-readable explanation without a second LLM call:

metric = AnswerRelevancyMetric(threshold=0.7, include_reason=True)
metric.measure(test_case)
print(metric.score, metric.reason)
# 0.62  "Response addresses the topic but omits the date asked for."

**RAGAS** uses a class-based metric API — instantiate metric classes and pass an `EvaluationDataset`. `llm=` is optional; omit it to use the configured default grader:

from ragas import evaluate
from ragas.metrics import Faithfulness, LLMContextRecall

result = evaluate(
    dataset,
    metrics=[Fait