# What is Retrieval Recall? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Information Retrieval  
> **Canonical Citation:** [https://spidits.com/ai-glossary/retrieval-recall](https://spidits.com/ai-glossary/retrieval-recall)

## Definition
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.

## Architectural Mechanics & 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.

## Industry Applications & Real-World Use Cases
Search engine tuning, RAG system evaluations, and information retrieval audits

## Frequently Asked Questions

### 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).

### How do you increase RAG recall?
By retrieving a larger number of candidates (higher K value) or utilizing query expansion and hybrid search.

## Related Terminology
- [rag](https://spidits.com/ai-glossary/rag)
- [semantic-search](https://spidits.com/ai-glossary/semantic-search)
- [retrieval-precision](https://spidits.com/ai-glossary/retrieval-precision)

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*From the SPIDITS AI Knowledge Base (300+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*