Contextual precision and contextual recall are metrics used to evaluate how effectively a retrieval-augmented generation (RAG) system retrieves and uses relevant information. Contextual precision measures how well the system prioritizes the most useful documents for a specific query. For example, if a user asks, “What are the symptoms of COVID-19?” and the system retrieves three highly relevant medical guidelines in the top results, followed by less relevant articles, its precision score reflects this ranking quality. High precision ensures the generator focuses on the best information first, reducing noise in the output. Low precision, however, means the system surfaces irrelevant or redundant documents early, which may lead the generator to produce less accurate answers.
Contextual recall, on the other hand, assesses whether the system retrieves all relevant information needed to address the query comprehensively. For instance, if a query about “treatment options for diabetes” requires details on medication, diet, and exercise, high recall means the system fetches documents covering all three aspects. Low recall indicates gaps—say, missing diet-related information—which could lead the generator to produce incomplete answers. Recall is critical for complex queries where omitting even one key detail can undermine the response. Together, precision and recall balance the system’s ability to prioritize and cover necessary content.
These metrics indicate distinct performance characteristics. High contextual precision suggests the retrieval component effectively ranks documents by relevance, which is crucial for real-time systems where processing all retrieved data is impractical. High contextual recall signals thoroughness, which matters for queries needing exhaustive answers. However, there’s often a trade-off: optimizing for precision (e.g., retrieving fewer documents) might sacrifice recall, and vice versa. Developers can use these metrics to tune retrieval parameters, like the number of documents fetched or the ranking algorithm, based on use-case priorities. For example, a medical chatbot might prioritize recall to avoid missing critical information, while a customer support tool might favor precision for faster, focused responses.
