The text discusses the evaluation of information retrieval systems using the Normalized Discounted Cumulative Gain (NDCG) metric. NDCG assesses retrieval quality by assigning ground truth ranks to database elements based on relevance, with higher penalties for irrelevant items ranked higher. The authors use Redis vector database and RedisVL as a python client library to evaluate two models: a base model and a fine-tuned model. The fine-tuned model outperforms the base model in terms of ranking performance and overall accuracy, achieving an average NDCG score of 0.60 compared to the base model's 0.49. The results indicate that the fine-tuned model frequently places the correct answer in the top rank and achieves better ranking performance.