Overview

Summary

These tutorials provide complete, working examples that you can adapt for your own use cases. Each tutorial includes:

• Full working code
• Explanation of key concepts
• Common pitfalls and how to avoid them
• Extensions and variations

Available Tutorials

🔍 RAG Evaluation Test retrieval quality, groundedness, and answer relevance in RAG systems

🤖 Testing Agents Validate multi-step agent workflows with tool usage and reasoning

💬 Chatbot Testing Test conversational flows, context handling, and response quality

🛡️ Content Moderation Implement safety checks and content filtering

What You’ll Learn

Through these tutorials, you’ll learn how to:

• Design effective test suites for different AI application types
• Combine built-in and custom checks for comprehensive validation
• Handle both single-turn and multi-turn scenarios
• Use LLM-as-a-judge for nuanced evaluation
• Track metrics and analyze test results
• Integrate checks into CI/CD pipelines

Prerequisites

Before starting these tutorials, you should:

• Have completed the Install & Configure guide
• Be familiar with the Core Concepts
• Have basic Python and async/await knowledge
• Have access to an LLM API (OpenAI, Anthropic, or compatible)

Getting Help

If you run into issues with these tutorials:

1. Check the Core Concepts for concept clarifications
2. Review the Custom Checks guide for check creation patterns
3. Look at the API reference for detailed documentation
4. Open an issue on GitHub if you find bugs or have suggestions