Rag Evaluation

What it does

This skill provides Retrieval Augmented Generation (RAG) evaluation capabilities. It allows you to assess the quality of your RAG system, identifying areas for improvement in retrieval and generation processes. Specifically, it helps measure faithfulness, relevance, and answer correctness within a RAG pipeline.

When to use it

• Debugging RAG pipelines: Identify why an AI agent is generating inaccurate or irrelevant responses by evaluating its RAG process.
• Comparing different RAG configurations: Test various retrieval methods (e.g., different embedding models, vector databases) and evaluate their impact on answer quality.
• Monitoring RAG performance over time: Track changes in RAG system effectiveness after updates or data modifications.
• Optimizing prompt engineering for RAG: Evaluate how prompt variations affect the faithfulness and relevance of generated answers.

Key capabilities

• Faithfulness evaluation
• Relevance assessment
• Answer correctness measurement
• RAG pipeline analysis

Example prompts

• "Evaluate the faithfulness of this RAG system's response: [response text] given these context documents: [context document list]"
• "Assess the relevance of the retrieved documents to the query: [query text] and the generated answer: [answer text]."
• “How correct is the following answer based on the provided context? Answer: [answer text], Context: [context text].”

Tips & gotchas

This skill requires a clear understanding of RAG architecture. Accurate evaluation relies on providing appropriate context documents and queries for assessment.