RAG Quality Calculator
Evaluate RAG response quality with RAGAS metrics: Faithfulness, Relevancy, Precision, and Recall.
How It Works
- Enter your data: Paste the question, retrieved context, and response generated by your RAG system.
- Automatic analysis: Our algorithm calculates the 4 RAGAS metrics: faithfulness, relevancy, precision, and recall.
- Interpret results: Identify weaknesses in your pipeline and get improvement recommendations.
Frequently Asked Questions
- What is the RAGAS score?
- RAGAS (Retrieval-Augmented Generation Assessment) is an open-source framework for evaluating RAG systems. It measures 4 dimensions: faithfulness (no hallucinations), answer relevancy, context precision, and context recall.
- How can I improve my faithfulness score?
- A low faithfulness score indicates hallucinations. To improve it: 1) Increase relevant context quantity, 2) Use a system prompt that emphasizes citations, 3) Reduce LLM temperature, 4) Switch to a more capable model like GPT-4 or Claude.
- What's the difference between context precision and recall?
- Precision measures if retrieved documents are relevant (avoid noise). Recall measures if all necessary documents were retrieved (avoid gaps). A good RAG system must optimize both.
- What score should I target for a production RAG system?
- Target an overall score above 0.7 for general use. For critical cases (medical, legal), aim for 0.85+. Faithfulness is the priority metric as it measures absence of hallucinations.
- How does this tool calculate scores?
- The tool uses heuristics based on text analysis: keyword overlap, entity detection, semantic structure analysis. For more accurate production evaluation, use the RAGAS library with an LLM judge.
- Can I use this tool to evaluate ChatGPT or Claude?
- This tool is designed for RAG systems where you control the context. For evaluating ChatGPT/Claude in standard mode (without RAG), context precision/recall metrics don't apply.
