DeepEval
An open-source LLM evaluation framework that enables automated unit testing
Open-source LLM evaluation framework from Confident AI that runs unit-test-style assertions on model outputs, with built-in metrics for hallucination, relevancy, and RAG faithfulness plus pytest integration for CI pipelines.
Key Features
- Hallucination detection metrics
- RAG faithfulness evaluation
- Answer relevancy analysis
- pytest test integration
- Continuous integration pipeline support
Pros
- Open-source, free, and flexible
- Supports many built-in evaluation metrics
- Easy to integrate into existing development workflows
Cons
- Requires basic programming and testing experience
- Relying on external APIs for evaluation may incur extra cost
Use Cases
- Validating retrieval and generation quality of RAG systems
- Automating LLM output testing in CI/CD pipelines
- Monitoring and evaluating chatbot answer accuracy
Editor's Note
This is an indispensable automated quality-assurance tool for developing and maintaining large language model applications.
FAQ
Is DeepEval free?
Yes. DeepEval is an open-source evaluation framework that developers can download and use for free in their projects.
Which evaluation metrics does it support?
It has built-in metrics such as hallucination, relevancy, and RAG faithfulness, making it easy to verify the quality of model outputs.
How do I integrate it into my development process?
It integrates directly with pytest, letting teams automatically run model tests in their continuous integration (CI) pipeline.