Retrieval Depth
Definition
Retrieval Depth refers to how far down an AI system searches within its candidate results before selecting passages for answer synthesis. Greater depth increases the likelihood of secondary sources being included in generative outputs.
Why It Matters
Shallow retrieval may favor dominant domains and limit emerging brand visibility.
How It Works
AI systems retrieve a ranked list of passages, and depth determines how many candidates are evaluated before final ranking.
Use Cases
- Improving competitive citation inclusion
- Evaluating RAG performance
- Auditing AI answer diversity
- Optimizing long-tail visibility
Best Practices
- Strengthen semantic clarity
- Improve entity authority
- Avoid thin content
- Enhance topical completeness
- Optimize chunk relevance
Frequently Asked Questions
What is retrieval depth? +
It defines how many candidate passages an AI system evaluates before ranking.
Why does depth matter? +
Greater depth improves citation diversity and inclusion chances.
Can brands influence depth? +
Indirectly through stronger semantic and authority signals.
Related Terms
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