---

name: llm-testing description: Testing patterns for LLM-based applications. Use when testing AI/ML integrations, mocking LLM responses, testing async timeouts, or validating structured outputs from LLMs. context: fork agent: test-generator version: 2.0.0 tags: [testing, llm, ai, deepeval, ragas, 2026] hooks: PostToolUse: - matcher: "Write|Edit" command: "$CLAUDE_PROJECT_DIR/.claude/hooks/skill/test-runner.sh" Stop: - command: "$CLAUDE_PROJECT_DIR/.claude/hooks/skill/eval-metrics-collector.sh"

LLM Testing Patterns

Test AI applications with deterministic patterns using DeepEval and RAGAS.

When to Use

• LLM integration testing
• Async timeout validation
• Structured output testing
• Quality gate testing
• RAG pipeline evaluation

Quick Reference

Mock LLM Responses

from unittest.mock import AsyncMock, patch

@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

DeepEval Quality Testing

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."],
)

metrics = [
    AnswerRelevancyMetric(threshold=0.7),
    FaithfulnessMetric(threshold=0.8),
]

assert_test(test_case, metrics)

Timeout Testing

import asyncio
import pytest

@pytest.mark.asyncio
async def test_respects_timeout():
    with pytest.raises(asyncio.TimeoutError):
        async with asyncio.timeout(0.1):
            await slow_llm_call()

Quality Metrics (2026)

Metric	Threshold	Purpose
Answer Relevancy	≥ 0.7	Response addresses question
Faithfulness	≥ 0.8	Output matches context
Hallucination	≤ 0.3	No fabricated facts
Context Precision	≥ 0.7	Retrieved contexts relevant

Anti-Patterns (FORBIDDEN)

# ❌ NEVER test against live LLM APIs in CI
response = await openai.chat.completions.create(...)

# ❌ NEVER use random seeds (non-deterministic)
model.generate(seed=random.randint(0, 100))

# ❌ NEVER skip timeout handling
await llm_call()  # No timeout!

# ✅ ALWAYS mock LLM in unit tests
with patch("app.llm", mock_llm):
    result = await function_under_test()

# ✅ ALWAYS use VCR.py for integration tests
@pytest.mark.vcr()
async def test_llm_integration():
    ...

Key Decisions

Decision	Recommendation
Mock vs VCR	VCR for integration, mock for unit
Timeout	Always test with < 1s timeout
Schema validation	Test both valid and invalid
Edge cases	Test all null/empty paths
Quality metrics	Use multiple dimensions (3-5)

Detailed Documentation

Resource	Description
references/deepeval-ragas-api.md	DeepEval & RAGAS API reference
examples/test-patterns.md	Complete test examples
checklists/llm-test-checklist.md	Setup and review checklists
templates/llm-test-template.py	Starter test template

Related Skills

• vcr-http-recording - Record LLM responses
• llm-evaluation - Quality assessment
• unit-testing - Test fundamentals

Capability Details

llm-response-mocking

Keywords: mock LLM, fake response, stub LLM, mock AI Solves:

• Mock LLM responses in tests
• Create deterministic AI test fixtures
• Avoid live API calls in CI

async-timeout-testing

Keywords: timeout, async test, wait for, polling Solves:

• Test async LLM operations
• Handle timeout scenarios
• Implement polling assertions

structured-output-validation

Keywords: structured output, JSON validation, schema validation, output format Solves:

• Validate structured LLM output
• Test JSON schema compliance
• Assert output structure

deepeval-assertions

Keywords: DeepEval, assert_test, LLMTestCase, metric assertion Solves:

• Use DeepEval for LLM assertions
• Implement metric-based tests
• Configure quality thresholds

golden-dataset-testing

Keywords: golden dataset, golden test, reference output, expected output Solves:

• Test against golden datasets
• Compare with reference outputs
• Implement regression testing

vcr-recording

Keywords: VCR, cassette, record, replay, HTTP recording Solves:

• Record LLM API responses
• Replay recordings in tests
• Create deterministic test suites