Why Evaluation Matters
Building a RAG system is only half the battle. To create a truly effective application, you need a robust framework for evaluating its performance. Proper evaluation helps you identify weaknesses, compare different configurations, and ensure your system provides accurate, relevant, and reliable answers.
Key RAG Metrics
Several key metrics are essential for assessing the quality of a RAG system. These can be broadly categorized into retrieval metrics and generation metrics.
Retrieval Metrics:
- Context Precision: Measures the signal-to-noise ratio of your retrieved documents. Are the retrieved chunks relevant to the query?
- Context Recall: Measures whether all the necessary information to answer the question was present in the retrieved context.
Generation Metrics:
- Answer Faithfulness: Measures how factually consistent the generated answer is with the provided context. It helps identify hallucinations.
- Answer Relevancy: Measures how relevant the generated answer is to the original user query.
Continuous Improvement
Evaluation is not a one-time task. It should be an integral part of your development lifecycle, enabling continuous improvement and ensuring your RAG system remains effective over time.
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