10 Common Pitfalls in RAG Evaluation and How to Avoid Them

RAG (Retrieval-Augmented Generation) pipelines, while complex, offer opportunities for improvement and success by addressing common pitfalls that arise from their intricate nature. These challenges often stem from overlooked details in development and evaluation, as well as trade-offs made during pipeline construction. Key issues include biased or inadequate retrievals, overemphasis on traditional generation metrics, lack of ground truth and contextual relevance, misalignment between training and evaluation, and underappreciation of user-centric metrics. Solutions involve adopting a balanced evaluation approach that integrates context-aware metrics, human judgments, and user feedback, while considering both retrieval and generation components. Additionally, using dynamic datasets, conducting thorough error analysis, and ensuring scalability and latency considerations are essential for robust RAG systems. The text emphasizes the importance of a holistic approach, focusing on both the microscopic details and the overall picture to achieve reliable and user-satisfying outcomes in RAG applications.