What is Retrieval Recall?
Retrieval Recall
Retrieval Recall is a search metric measuring the percentage of relevant documents successfully retrieved from a database relative to all existing relevant documents. High recall ensures the LLM receives all context needed to answer a query.
Why It Matters for AI Builders
Determines the context-augmented retrieval precision for search engine tuning, rag system evaluations, and information retrieval audits; mastering Retrieval Recall allows builders to feed clean database sources to models, minimizing hallucinations.
Detailed Deep Dive
Retrieval Recall is an information retrieval measurement that calculates the proportion of relevant documents retrieved from a database relative to the total number of relevant documents available. In RAG architectures, high recall ensures that the LLM has all the necessary source context to generate a complete answer, preventing errors of omission.
Frequently Asked Questions
Q:What is the difference between Recall and Precision?
Recall measures completeness (finding all relevant items). Precision measures purity (how many retrieved items are actually relevant).
Q:How do you increase RAG recall?
By retrieving a larger number of candidates (higher K value) or utilizing query expansion and hybrid search.
Quick Facts
- CategoryInformation Retrieval
- Key ApplicationSearch engine tuning, RAG system evaluations, and information retrieval audits
Coverage Trend12 Weeks
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