Does this skill support structured output validation?

Yes, it includes specific patterns for validating JSON schemas and ensuring that LLM responses match your application's expected data structure.

What are the recommended quality metrics for AI applications?

This skill prioritizes Answer Relevancy (≥0.7), Faithfulness (≥0.8), low Hallucination scores (≤0.3), and high Context Precision (≥0.7) to ensure high-quality outputs.

Can I use this for RAG (Retrieval-Augmented Generation) applications?

Absolutely. It integrates with RAGAS and DeepEval specifically to measure retrieval quality, context usage, and the accuracy of generated answers based on provided context.

Why shouldn't I test against live LLM APIs in CI?

Testing against live APIs is slow, expensive, and non-deterministic, often leading to flaky CI/CD pipelines and unpredictable costs. Mocking or using VCR recordings provides faster, reliable results.

AI & LLM Testing Patterns

Name: AI & LLM Testing Patterns
Author: yonatangross

byyonatangross

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Security & Testing

Implements deterministic testing patterns for AI applications using DeepEval, RAGAS, and advanced mocking strategies.

About

This skill provides a comprehensive toolkit for validating LLM-based applications by enforcing industry-standard best practices like response mocking, quality metric evaluation, and asynchronous timeout handling. It enables developers to transition from flaky, expensive live API tests to deterministic unit and integration tests using frameworks like DeepEval and VCR.py. By automating the validation of structured outputs and RAG pipelines, it ensures that AI-driven features meet production-grade standards for accuracy, faithfulness, and reliability.

Key Features

29 GitHub stars
Structured JSON output and schema verification
DeepEval and RAGAS integration for quality metrics (Faithfulness, Relevancy)
Deterministic LLM response mocking and stubbing
Automated async timeout and error handling validation
VCR.py recording for reliable, offline integration tests

Use Cases

Validating RAG pipeline accuracy using Faithfulness and Context Precision metrics
Creating cost-effective CI/CD pipelines by mocking expensive LLM API calls
Ensuring AI-generated JSON outputs strictly adhere to defined application schemas

About

Key Features

29 GitHub stars
Structured JSON output and schema verification
DeepEval and RAGAS integration for quality metrics (Faithfulness, Relevancy)
Deterministic LLM response mocking and stubbing
Automated async timeout and error handling validation
VCR.py recording for reliable, offline integration tests

Use Cases

Validating RAG pipeline accuracy using Faithfulness and Context Precision metrics
Creating cost-effective CI/CD pipelines by mocking expensive LLM API calls
Ensuring AI-generated JSON outputs strictly adhere to defined application schemas