How to use DeepEval for RAG Testing

DeepEval is a specialized testing framework that treats LLM outputs like software code. It allows you to run unit tests on your RAG pipeline to catch hallucinations and accuracy drops.

Implementing Metric-Based Tests

With DeepEval, you can define "assertions" for your AI. You can test for "Answer Relevancy" to ensure the AI isn't wandering off-topic, or "Contextual Precision" to verify that your vector database is actually finding the right documents. These tests provide a numeric score, allowing you to track your application's quality over time.

Continuous Integration for AI

By integrating DeepEval into your GitHub Actions or CI/CD pipeline, you can automatically test every prompt update or model change. If a new prompt version causes a drop in the "Faithfulness" score, the build fails, preventing you from pushing low-quality AI responses to your production users.