How to Calculate RAG Recall at K
Retrieval-augmented generation (RAG) systems rise or fall on whether relevant evidence shows up inside the model's prompt window. Recall@K is the foundational metric: it quantifies the share of ground-truth documents that the retriever surfaces within the top K results. This walkthrough formalises the calculation for advanced practitioners, detailing variable definitions, equations, workflow controls, and validation patterns that keep evaluations reproducible across experiments and deployments.
We connect Recall@K with complementary signals—Precision@K and the Fβ score—so you can balance coverage and noise when tuning retrieval depth or reranker policies. Examples show how to interpret the metric for multi-label corpora, highly imbalanced knowledge bases, and evolving data. The guide complements latency and cost analyses discussed in the GenAI P95 latency budget walkthrough and training economics covered in the GPU training time and cost guide, ensuring evaluation remains anchored to user experience and infrastructure budgets.
Definition and motivation
Recall@K measures the fraction of relevant documents retrieved within the first K results for a given query. If the ground truth identifies R relevant passages and the retriever returns rK of them in the top K, then Recall@K = rK ÷ R. High recall ensures the generator sees answer-bearing evidence, reducing hallucinations and enabling grounded responses. Because K mirrors your context window or reranker cut-off, the metric directly connects to engineering decisions about embedding dimensionality, ANN index parameters, and batching strategies.
Recall@K should be tracked alongside other quality indicators such as answer exact-match, citation acceptance rates, and latency budgets. Without adequate recall, improvements elsewhere stall because the model never observes the right facts. Conversely, pushing recall too high without managing precision floods the prompt with noise. The workflow below balances both dimensions through optional precision and Fβ scoring.
Variables, symbols, and units
Establish a labelled evaluation dataset for each domain. Use integer counts for documents and dimensionless ratios for derived metrics.
- R – Number of relevant documents in the corpus for a query.
- rK – Relevant documents retrieved within the top-K results.
- K – Number of documents returned to the generator or reranker (optional when you only track relevant hits).
- β – Weighting parameter for the Fβ score. β > 1 emphasises recall, β < 1 emphasises precision.
- Recall@K – rK ÷ R. Precision@K – rK ÷ K. Fβ – Combined harmonic mean using β.
Record supporting context: embedding version, index hyperparameters, reranker model, and query sampling method. These metadata points enable apples-to-apples comparisons across experiments and production deployments.
Formula recap
Recall@K = rK ÷ R
Precision@K = rK ÷ K (when K is provided)
Fβ = (1 + β²) × (Precision@K × Recall@K) ÷ (β² × Precision@K + Recall@K)
Keep Recall@K within [0, 1]. Because rK cannot exceed R, clamp values at R when noisy labelling produces over-counts. When K is omitted, treat Precision@K as 1 for non-zero recall and 0 otherwise—this mirrors the calculator's behaviour and maintains continuity for Fβ scoring.
Compute standard deviations across queries to understand evaluation variance. For corpora with sparse relevant documents (R = 1 or 2), small misclassifications swing Recall@K dramatically; stratify reporting by difficulty tiers to contextualise results.
Step-by-step workflow
Step 1: Curate and label the evaluation set
Assemble at least several hundred query-document pairs. Human labelers should flag every passage that independently answers the query. When using weak supervision, audit a stratified sample to ensure label noise remains acceptable. Store references (document IDs, timestamps, annotator IDs) for reproducibility.
Step 2: Run retrieval and capture ranked results
Execute your retriever (vector index, hybrid BM25 + dense, or learned reranker) on the evaluation queries. For each query, log the ranked document IDs up to the maximum K you care about. Freeze embeddings and index parameters during the run so results remain deterministic.
Step 3: Count hits and compute Recall@K
Intersect the ranked list with the labelled relevant set. rK equals the number of relevant IDs appearing at ranks ≤ K. Divide by R to get Recall@K, and divide by K to compute Precision@K when K is specified. Capture β upfront if product stakeholders weigh recall differently from precision.
Step 4: Validate against baselines
Compare results with prior checkpoints or baseline retrieval strategies. If recall drops more than a few percentage points, inspect embedding drift, query normalization, or index update cadence. Use telemetry from LLM inference carbon intensity studies to ensure improved recall does not blow up energy budgets via larger prompt windows.
Validation and QA
Perform consistency checks before publishing results. Confirm that the sum of rK across queries never exceeds the sum of R—if it does, duplicates or overlapping passages likely skew labels. Plot Recall@K distributions to spot bimodal behaviour that may signal domain drift. Re-run the evaluation with shuffled seed ordering to confirm the pipeline is deterministic.
Maintain a notebook or version-controlled script that imports the same calculator logic used here. That ensures manual calculations and automated dashboards stay aligned. Include unit tests for edge cases such as R = 0 (skip the query), rK = 0 (precision and recall should be zero), and K smaller than rK (indicates labelling mismatch).
Limits and interpretation
Recall@K is sensitive to labelling completeness. If annotators miss relevant passages, the metric underestimates retriever quality. Conversely, when knowledge bases change rapidly, stale labels inflate recall. Mitigate both issues by refreshing evaluation sets each release cycle and logging drift metrics in your experiment tracker. Consider weighting recall by business importance (for example, critical compliance questions) when reporting to executives.
The metric does not account for partial overlap between passages and questions. Pair it with downstream answer quality metrics and human review before shipping retrieval updates. When scaling to multilingual corpora, compute recall per language and macro-average; otherwise high-resource languages may mask low-resource regressions.
Embed: RAG Recall@K calculator
Use the embedded calculator to enter relevant document counts, retrieved hits, and optional K or β values. The tool outputs Recall@K, Precision@K, and the chosen Fβ score with consistent rounding so you can paste the results into evaluation reports or CI dashboards.