To measure the accuracy of a retrieval component in a RAG system, developers commonly use precision@K and recall@K to evaluate how well the system retrieves relevant documents. Precision@K measures the proportion of top-K retrieved documents that are actually relevant, while recall@K measures how many of the total relevant documents were captured within those K results. For example, if a query has 10 relevant documents in a corpus, and the retriever returns 5 documents (K=5) with 3 being relevant, precision@5 is 3/5 (60%) and recall@5 is 3/10 (30%). These metrics directly quantify the trade-off between retrieving high-quality results (precision) and covering all possible relevant items (recall).
To implement these metrics, you need a labeled dataset where each query is paired with a known set of relevant documents. For each query, run the retriever to fetch the top-K documents and compare them against the ground truth. For instance, in a code search scenario, if a user queries “Python CSV parsing” and the correct answers include the csv module documentation and a Stack Overflow snippet about pandas.read_csv, precision@3 would check if those two appear in the top 3 results. If only the csv module is retrieved, precision@3 is 1/3 (~33%) and recall@3 is 1/2 (50%). Tools like Python’s rank_eval library or custom scripts can automate these calculations across a test dataset.
However, precision@K and recall@K have limitations. They assume a static cutoff (K) and don’t account for the ranking order beyond that point. For example, if the most relevant document is ranked 4th in a K=3 setup, it’s treated as irrelevant, even though it’s just outside the cutoff. To address this, developers often combine these metrics with others like mean reciprocal rank (MRR) or mean average precision (MAP), which weigh the position of relevant results. Additionally, real-world systems may prioritize one metric over the other: a customer support chatbot might favor higher recall to avoid missing critical answers, while a search engine might prioritize precision to ensure top results are reliable. Testing with varied K values (e.g., K=5, 10) and analyzing the precision-recall curve helps balance these goals.