Retrieval Recall
Definition
Retrieval Recall measures how effectively an AI system retrieves all relevant content from its index. High recall indicates that important passages are not missed during the retrieval phase of AI answer generation.
Why It Matters
Low recall reduces citation opportunities and limits AI visibility.
How It Works
Recall is calculated as the proportion of relevant documents retrieved compared to the total relevant documents available.
Use Cases
- Evaluating RAG pipelines
- Improving citation frequency
- Auditing AI answer coverage
- Optimizing content chunking
Best Practices
- Use clear semantic headings
- Improve topical depth
- Avoid fragmented content
- Optimize chunk structure
- Update stale content
Frequently Asked Questions
What is retrieval recall? +
It measures how many relevant documents are successfully retrieved by an AI system.
Why is recall important? +
Higher recall increases the chance of content being cited in AI answers.
How can recall be improved? +
Through better semantic structure and chunk optimization.
Related Terms
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