How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k

In the third and final part of a series on evaluating retrieval quality in RAG pipelines, the article emphasizes the importance of graded measures such as DCG@k and NDCG@k. These metrics are essential for determining how well retrieval systems perform, which directly impacts the effectiveness of AI applications that rely on accurate information retrieval. The publication, featured on Towards Data Science, aims to equip practitioners with the tools necessary to enhance their RAG pipelines, ensuring that they can deliver high-quality results. This focus on evaluation methods reflects a broader trend in AI development, where the accuracy and reliability of information retrieval are paramount for user satisfaction and system performance.