Recall at k is the retrieval metric that measures whether relevant documents are present within the top-k returned results - it quantifies coverage of needed evidence.

What Is Recall at k?

• Definition: Proportion of relevant items recovered in the first k retrieved candidates.
• Binary Variant: For single-answer tasks, often treated as hit or miss at top-k.
• Sensitivity Profile: Emphasizes not missing relevant evidence, regardless of rank position within k.
• RAG Relevance: High recall is prerequisite for answerable grounded generation.

Why Recall at k Matters

• Answer Feasibility: If no relevant passage is retrieved, generation cannot be reliably correct.
• Retriever Coverage: Detects blind spots in query understanding and index representation.
• Model Comparison: Useful first-pass metric for candidate retriever evaluation.
• Pipeline Tuning: Guides top-k size and hybrid retrieval design choices.
• Safety Role: Better recall reduces unsupported fallback to parametric guesses.

How It Is Used in Practice

• k Sweep Analysis: Measure recall across multiple k values to find diminishing returns.
• Segment Diagnostics: Break down recall by query type and domain difficulty.
• Joint Evaluation: Pair with precision and rank metrics for balanced optimization.

Recall at k is a foundational coverage metric in retrieval evaluation - strong recall is essential to ensure relevant evidence is available for downstream grounded answer generation.