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Ragas

evaluation
Adopt

Ragas is a framework for evaluating and monitoring Retrieval Augmented Generation (RAG) applications. It provides comprehensive metrics and tools to assess the quality, reliability, and performance of AI systems that combine language models with external knowledge bases. However, based on our team's experience, Ragas has notable limitations in practice, and we generally recommend Deepeval as a more user-friendly and robust alternative for most use cases.

Key Features

  • Automated evaluation of RAG pipelines with industry-standard metrics
  • Built-in support for popular vector databases and embedding models
  • Comprehensive scoring for context relevance, answer faithfulness, and retrieval quality
  • Integration with major LLM platforms and frameworks
  • Detailed analytics and visualization capabilities

Example Ragas Output Example: Ragas output showing evaluation metrics, semantic similarity, and answer correctness for a sample RAG pipeline.

Business Value

  • Improve accuracy and reliability of AI applications
  • Reduce hallucinations and incorrect responses
  • Optimize retrieval strategies and knowledge base quality
  • Monitor production RAG systems in real-time
  • Make data-driven decisions about model and retrieval improvements

Use Cases

  • Evaluating the quality of RAG pipelines
  • Research scenarios requiring custom or experimental metrics
  • Projects where legacy Ragas integration already exists

Integration

  • Requires manual configuration for most metrics and workflows
  • Integrates with LangChain and other Python LLM frameworks
  • Output can be exported for further analysis

Team Insights

Our team has encountered several significant challenges while working with Ragas:

  1. Documentation and Support:
  • Documentation contains significant gaps and errors
  • Steep learning curve for new team members
  • Certain important features like Persona and custom prompts are not well higlighted
  • Lack of clear versioning and backward compatibility
  1. Technical Challenges:
  • Complex setup required for both local development and CI/CD pipeline integration
  • Upgrading to newer versions broke the setup on certain occasions
  • No built-in visualization tools for evaluation results analysis
  1. Evaluation Reliability:
  • Evaluations often lack transparency in decision-making
  • No clear explanation of how the tool reaches conclusions
  • Results can be inconsistent across different runs
  1. Integration Issues:
  • Extra overhead to implement reporting for CI/CD runs
  • Requires extensive manual configuration

Recommendation

For most evaluation scenarios, especially those focused on RAG or basic LLM output quality, we recommend using Deepeval. Ragas may be considered if you need very specific custom metrics or are already invested in its ecosystem, but be prepared for additional configuration effort and documentation issues.

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