RAG Evaluation

Overview

Retrieval-Augmented Generation (RAG) systems combine information retrieval with AI generation to answer questions based on organizational knowledge bases. Evaluation methods assess both retrieval quality and answer generation accuracy.

What RAG Evaluation Measures

Retrieval Quality

• Are the right documents/passages retrieved for each question?
• How relevant is the retrieved information?
• Are important sources being missed?

Answer Quality

• Do generated answers accurately reflect retrieved information?
• Are answers factually correct and verifiable?
• Is the response relevant to the user's question?

System Performance

• How quickly does the system respond?
• Does quality remain consistent across different question types?
• Are answers appropriately scoped (not too brief, not too verbose)?

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Use Cases

RAG evaluation will support:

• Customer support assistants accessing product documentation
• Semantic search across medical instructions and training videos
• Learning assistants for educational content
• Knowledge base question-answering systems

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For updates on RAG evaluation methods, check the GAIK GitHub repository.