DeepEval

What it does

DeepEval is a unit-testing framework for LLM applications and RAG pipelines. It provides 14+ research-backed evaluation metrics that automatically grade language model responses for hallucinations, answer relevancy, faithfulness, toxicity, and context recall on a 0.0 to 1.0 scale.

Why it’s interesting

LLM outputs are non-deterministic, making traditional unit tests inadequate. DeepEval brings scientific rigor to GenAI development by enabling automated testing in CI/CD pipelines with detailed qualitative reasoning for each evaluation result.

Real-world use case

Integrate DeepEval into your GitHub Actions pipeline. On every PR touching prompts or retrieval logic, automatically run regression tests to ensure faithfulness to retrieved context exceeds 0.80, preventing hallucinations from reaching production.

What I can learn

• 01

  Automated LLM-as-a-Judge Design

  Study how G-Eval and chain-of-thought rubrics structure evaluation prompts to accurately score subjective criteria with numeric scores and justifications.

• 02

  Pytest Test Runner Extension

  Learn how DeepEval hooks into Pytest's execution lifecycle to provide pass/fail thresholds for non-deterministic AI outputs.

• 03

  Synthetic Dataset Synthesis

  Understand how to automatically generate realistic Q&A test cases from raw documents to benchmark AI applications before production.

One thing to explore

Faithfulness and Answer Relevancy Metrics

Inspect the metrics folder to see how contradiction detection and claim extraction are implemented using LLM judge prompts.