DeepEval: Unit Testing for LLMs

If you don't test your LLM output, you don't have a product; you have a science project. DeepEval is the "pytest" for AI, providing a set of metrics—like factual accuracy, semantic similarity, and relevance—that allow you to build automated unit tests for your LLM.

Semantic Metrics

DeepEval uses an "evaluator LLM" to compare your model's output against a reference, scoring it on metrics like hallucination rate or answer relevance. This allows you to verify that your agent is actually answering the question correctly, rather than just sounding confident.

Continuous Integration

By incorporating these unit tests into your CI/CD flow, you ensure that every prompt change or model update remains faithful to your requirements, preventing silent regressions and ensuring long-term product quality.