Evaluating retrieval systems with NDCG@10 is a common pain point for teams building search or recommendation pipelines. The main challenges are: obtaining a reliable relevance baseline, understanding how much a reranker actually improves ranking quality, and keeping the evaluation reproducible without heavy engineering overhead. A practical way to tackle these issues is to start with a clear, reproducible script that computes NDCG@10 for both a bi‑encoder retriever and a downstream reranker. First, encode each query with the bi‑encoder, fetch the top‑k documents from the corpus, and extract the ordered list of corpus IDs. Then, compute discounted cumulative gain (DCG) using the relevance scores supplied for each query, and derive ideal DCG by sorting the relevance values in descending order. The NDCG@10 score is the ratio of the obtained DCG to the ideal DCG. Apply the same process after reranking: feed the initial bi‑encoder ranking into the reranker, obtain a new order, and recompute NDCG@10. By logging both scores per query and reporting the mean across the evaluation set, you instantly see the reranking lift. This approach isolates the impact of the reranker, highlights queries where the bi‑encoder fails, and gives a concrete number to guide model iteration. Keeping the evaluation code in a single, well‑commented script ensures that experiments stay comparable and that improvements can be traced back to specific changes in the retriever or reranker.
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