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

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.

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=[Faithfulness(), LLMContextRecall()],
)

Bump floors: deepeval >= 4.0, ragas >= 0.4.

House rules the vendor docs do not state (the inverted HallucinationMetric threshold, the gpt-5-mini grader default, the 95 percent confidence-interval recipe, and the latency, quality-gate and truncation numbers) are recorded in references/ork-delta.md. Read that before writing either library's setup code.

Quality Metrics Thresholds

Structured Output Validation

Always validate LLM output with Pydantic schemas:

from pydantic import BaseModel, Field

class LLMResponse(BaseModel):
    answer: str = Field(min_length=1)
    confidence: float = Field(ge=0.0, le=1.0)
    sources: list[str] = Field(default_factory=list)

async def test_structured_output():
    result = await get_llm_response("test query")
    parsed = LLMResponse.model_validate(result)
    assert 0 <= parsed.confidence <= 1.0

VCR.py for Integration Tests

Record and replay LLM API calls for deterministic integration tests:

@pytest.fixture(scope="module")
def vcr_config():
    import os
    return {
        "record_mode": "none" if os.environ.get("CI") else "new_episodes",
        "filter_headers": ["authorization", "x-api-key"],
    }

@pytest.mark.vcr()
async def test_llm_integration():
    response = await llm_client.complete("Say hello")
    assert "hello" in response.content.lower()

Agentic Test Workflow

The three-agent pattern for end-to-end test automation:

Planner -> specs/*.md -> Generator -> tests/*.spec.ts -> Healer (auto-fix)

1. Planner: Explores your app and produces Markdown test plans. Owned by the testing-e2e skill (rules/e2e-ai-agents.md); the CLI and its generated files are documented at https://playwright.dev/docs/test-agents.

2. Generator: Converts Markdown specs into Playwright tests, validating selectors against the running app. Also owned by testing-e2e (rules/e2e-ai-agents.md); the locator ladder it follows lives in testing-e2e rules/e2e-playwright.md.

3. Healer (references/healer-agent.md): Automatically fixes failing tests by replaying failures, inspecting the DOM, and patching locators/waits. Max 3 healing attempts per test.

Agent initialization is CLI-only (npx playwright init-agents); there is no config key for it. Only the healing stage keeps a local reference, because its 3-attempt ceiling and its refusal to touch test logic are house limits rather than vendor defaults.

Edge Cases to Always Test

For every LLM integration, cover these paths:

• Empty/null inputs -- empty strings, None values
• Long inputs -- truncation behavior near token limits
• Timeouts -- fail-open vs fail-closed behavior
• Schema violations -- invalid structured output
• Prompt injection -- adversarial input resistance
• Unicode -- non-ASCII characters in prompts and responses

See checklists/llm-test-checklist.md for the complete checklist.

Anti-Patterns

Related Skills

• ork:testing-unit — Unit testing fundamentals, AAA pattern
• ork:testing-integration — Integration testing for AI pipelines
• ork:golden-dataset — Evaluation dataset management
• ork:testing-e2e owns the Planner and Generator agent workflow and the Playwright locator ladder
• ork:testing-perf owns the latency and load budgets referenced in references/ork-delta.md