Contextual precision and contextual recall are metrics used to evaluate the performance of retrieval systems in Retrieval-Augmented Generation (RAG) pipelines. They focus on how effectively the system retrieves context relevant to a query and how comprehensively it captures necessary information for generating accurate answers. Here’s how they work and what they indicate:
Contextual Precision measures the proportion of retrieved context that is directly relevant to the query. For example, if a system retrieves 10 document chunks for a question about "neural network architectures," and 7 of them discuss architectures while 3 cover unrelated topics like hardware optimization, the contextual precision is 70%. This metric highlights the system’s ability to filter out noise, ensuring the generator isn’t misled by irrelevant information. High precision reduces the risk of the model producing answers based on incorrect or tangential context, improving reliability. However, overly strict filtering might exclude marginally relevant but useful details, so balancing precision with recall is critical.
Contextual Recall assesses how much of the necessary context for answering the query is retrieved. If the ideal answer requires information from 8 relevant documents, but the system only retrieves 5, the recall is 62.5%. This metric reflects the system’s ability to avoid missing critical information. For instance, if a user asks about "health impacts of caffeine," missing studies linking caffeine to sleep disorders would lower recall, potentially leading to incomplete answers. High recall ensures the generator has sufficient data to produce comprehensive responses, but excessive retrieval of marginally relevant content can harm precision.
Together, these metrics diagnose retrieval quality. High precision and recall indicate a system that retrieves most necessary information with minimal noise. For example, a medical RAG system with 90% precision and 85% recall would reliably provide accurate answers without omitting key details. Conversely, low precision risks hallucinations from irrelevant context, while low recall leads to gaps in knowledge. Developers use these metrics to optimize retrieval parameters (e.g., chunk size, ranking algorithms) and balance trade-offs. For instance, increasing the number of retrieved documents might improve recall but hurt precision, requiring adjustments like better embedding models or reranking steps.