Retrieval Recall Calculator
Calculate recall@k for a retrieval system used in RAG pipelines.
Inputs
Recall@k
0.7000
Precision@k
0.7000
Step by step
Recall@k: relevant_retrieved ÷ total_relevant
7 ÷ 10
= 0.7000
Precision@k: relevant_retrieved ÷ k
7 ÷ 10
= 0.7000
How it works
Recall@k measures what fraction of all relevant documents appear in the top-k retrieved results. In RAG, high recall@k is critical because any relevant information not retrieved cannot be used by the LLM to generate its answer.
Formula
Recall@k
recall@k = |relevant ∩ top_k| / |total_relevant|
- k
- Number of results retrieved
Frequently Asked Questions
What recall@k should I target for RAG?
Aim for recall@10 > 0.9 or recall@20 > 0.95. If the relevant chunk isn't retrieved, the LLM cannot use it regardless of its reasoning capability.
How do I improve retrieval recall?
Use hybrid search (dense + sparse/BM25), re-ranking, query expansion, smaller chunks with overlap, or fine-tuned embedding models trained on your domain.