🎯 RAG Retrieval Metrics Calculator
Calculate Precision@K, Recall@K, hit rate, MRR, average precision, DCG, and NDCG from ranked RAG retrieval relevance judgments.
Calculate RAG Retrieval Metrics
What this RAG retrieval metrics calculator calculates
RAG retrieval should be evaluated before answer generation so teams can distinguish retrieval failures from generation failures. Precision@K measures how much of the retrieved context is relevant, while Recall@K measures how much of all known relevant content was recovered.
Reciprocal rank rewards placing the first relevant result early. Average precision rewards ranking multiple relevant results early. NDCG supports graded relevance, making it useful when some passages are highly useful and others are only partially relevant.
RAG Retrieval Metrics Calculator formula
Assumptions and limitations
The result is an engineering estimate based on the values entered. Model architectures, tokenizers, frameworks, accelerators, parallel strategies, billing rules, and production traffic differ. Validate important decisions with measured data from the exact model, runtime, hardware, and workflow you intend to use.
Worked example
For relevance scores 3, 0, 2, 1, 0 at K=5 and five relevant documents overall, three retrieved results meet a threshold of one. Precision@5 is 0.60, Recall@5 is 0.60, and reciprocal rank is 1 because the first result is relevant.
How to interpret and use the result
Aggregate metrics across a representative query set and segment by task, language, source, freshness, and query difficulty. Choose K based on the downstream context budget. High recall with low precision can waste tokens and distract generation; high precision with low recall can omit necessary evidence.
- Start with measured or representative production assumptions.
- Run conservative, expected, and optimistic scenarios.
- Record model version, framework, precision, hardware, and review date.
- Replace estimates with observed p50 and p95 values after testing.
Common mistakes to avoid
- Evaluating only final answer quality without labeling retrieved evidence.
- Using total relevant equal to retrieved relevant, which makes recall misleading.
- Comparing NDCG across datasets with inconsistent relevance scales or judgment depth.
Frequently asked questions
What relevance values should I enter?
Use 0/1 for binary relevance or a consistent graded scale such as 0 to 3 when some passages are more useful than others.
Why can Precision@K and Recall@K move in opposite directions?
Increasing K may recover more relevant documents while also retrieving additional irrelevant documents.
What is a good RAG retrieval score?
There is no universal threshold. Compare against task requirements, a baseline retriever, and downstream answer quality under the same context budget.
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Methodology and privacy
This educational tool uses the formula and assumptions displayed on the page. Calculations run locally in your browser, and the page does not transmit the values you enter. Results are estimates rather than provider quotes, benchmark guarantees, financial advice, or capacity guarantees. Last methodology review: August 2, 2026.
Formula Explorer connections
Interpretation: This relationship connects document segmentation and vector retrieval to index size, context use, retrieval quality or generation cost. Assumption: Embedding model, chunk overlap, top-k, corpus characteristics and evaluation labels strongly influence the practical result.