ragas

To evaluate a RAG pipeline, you measure key metrics such as faithfulness, answer relevancy, context precision, and context recall. This process identifies factual weaknesses in generated answers and retrieves relevant context for user queries.

Faithfulness in RAG evaluation measures whether the generated answer is factually correct and consistent with the provided context. It ensures the retrieval augmented generation pipeline does not hallucinate information outside the source documents.

Yes, you can assess EU AI Act compliance by evaluating AI system performance and risk mitigation against Article 15 requirements. This involves measuring RAG pipeline quality metrics to implement necessary performance risk controls.

To test if a RAG chatbot gives relevant answers, you evaluate answer relevancy and context precision metrics. This verifies that the chatbot's responses are factually correct based on provided context and relevant to the user's query.

Context precision and context recall are RAG metrics measuring retrieval quality. Context precision evaluates if the retrieved context is relevant to the query, while context recall checks if all necessary information was successfully retrieved from the source.

You identify weaknesses in a RAG system by running objective performance analysis on faithfulness, answer relevancy, context precision, and context recall. This evaluation mitigates performance risks by pinpointing exactly where the pipeline fails.