Evaluating RAG Part I: How to Evaluate Document Retrieval

Evaluating Retrieval Augmented Generation (RAG) components is crucial for improving their performance, especially in the context of large language models (LLMs). The evaluation process involves assessing the quality of a system, which can be subjective and relative to specific use cases. Metrics play a significant role in evaluating RAG pipelines, particularly when it comes to retrieving relevant documents from a database. Various metrics such as recall, mean reciprocal rank (MRR), and mean average precision (mAP) are used to assess the performance of the retriever component. By understanding these metrics and their applications, developers can identify areas for improvement and refine their RAG systems accordingly. Effective evaluation is essential for achieving better results in downstream applications, making it a critical component of machine learning projects.