DeepEval
Pytest-style evaluation framework for LLM applications with automated metrics for RAG accuracy, hallucination detection, and safety testing.
Technical Architecture & Overview
DeepEval is an open-source evaluation framework for LLM applications that works like Pytest for AI. It includes metrics for RAG evaluation, hallucination detection, and safety testing, with built-in CI/CD integration for continuous LLM testing. Red teaming features are handled by the separate DeepTeam package from the same maintainers.
Targeted Technical Use Cases
Running Pytest-style LLM evaluation suites in CI/CD pipelines covering accuracy, safety, and hallucination metrics.
Evaluation & Trade-offs
Core Strengths
- +Pytest-compatible API, making it familiar to Python developers.
- +Comprehensive metrics covering RAG, hallucination, and safety evaluation.
- +Built-in CI/CD integration with detailed HTML test reports.
Trade-Offs & Limitations
- -LLM-graded metrics can incur API costs and introduce evaluation variability.
- -Requires Python test definitions for each evaluation scenario.
Defensive Security Application
Continuous evaluation of LLM applications for hallucination, safety, and accuracy in CI/CD.
Frequently Asked Questions
What is DeepEval?→
DeepEval is an open-source evaluation framework for LLM applications that works like Pytest for AI. It includes metrics for RAG evaluation, hallucination detection, and safety testing, with built-in CI/CD integration for continuous LLM testing. Red teaming features are handled by the separate DeepTeam package from the same maintainers.
What is DeepEval used for?→
Running Pytest-style LLM evaluation suites in CI/CD pipelines covering accuracy, safety, and hallucination metrics.
What are the strengths of DeepEval?→
- +Pytest-compatible API, making it familiar to Python developers.
- +Comprehensive metrics covering RAG, hallucination, and safety evaluation.
- +Built-in CI/CD integration with detailed HTML test reports.
What are the limitations of DeepEval?→
- +LLM-graded metrics can incur API costs and introduce evaluation variability.
- +Requires Python test definitions for each evaluation scenario.
How is DeepEval used defensively?→
Continuous evaluation of LLM applications for hallucination, safety, and accuracy in CI/CD.