Graphrag Evaluation

What it does

This skill, graphrag-evaluation, provides a mechanism to evaluate Retrieval Augmented Generation (RAG) pipelines. It assesses the quality of retrieved documents and their relevance to user queries within a RAG system. The evaluation process helps identify areas for improvement in both retrieval and generation components.

When to use it

• Debugging RAG performance: Use this skill when your RAG application is producing inaccurate or irrelevant responses.
• Optimizing Retrieval Strategies: Evaluate different retrieval methods (e.g., keyword search, semantic search) to determine which yields the best results for specific query types.
• Improving Document Relevance: Assess whether the retrieved documents are truly relevant to the user's question and contributing to a helpful answer.
• Benchmarking RAG Systems: Compare the performance of different RAG configurations or models using standardized evaluation metrics.

Key capabilities

• Evaluates RAG pipelines.
• Assesses quality of retrieved documents.
• Determines relevance of retrieved documents to user queries.
• Identifies areas for improvement in retrieval and generation components.

Example prompts

• "Evaluate the RAG pipeline's response to this query: 'What are the symptoms of a common cold?' using these retrieved documents..."
• "Assess the relevance of these documents to the question, 'How does photosynthesis work?'"
• "Can you score the quality and relevance of the following document in relation to the prompt, 'Explain the concept of blockchain'?"

Tips & gotchas

The skill’s effectiveness relies on providing it with both the user query and the retrieved documents for evaluation. Ensure the documents are properly formatted or structured for optimal analysis.