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IMMUNITYSEC

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.
  • +Metrics covering RAG, hallucination, and safety evaluation.
  • +Built-in CI/CD integration, with shareable test reports through the Confident AI platform.

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.
  • +Metrics covering RAG, hallucination, and safety evaluation.
  • +Built-in CI/CD integration, with shareable test reports through the Confident AI platform.
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.