Knowledge & Data · Software component
Retrieval Confidence Filter
Software componentKnowledge & DataKnowledge & Dataarc:RetrievalConfidenceFilter
A retrieval post-processing component that scores retrieved candidates for relevance confidence and discards marginal matches below a threshold, signalling when no sufficiently relevant context exists.
Responsibility. Prevents low-confidence retrieved context from reaching the prompt.
Also known as: Retrieval quality threshold, Retrieval quality scoring
Relationships
receives data from dynamic
- Reranker abstract Ch3.10
- Vector Retriever abstract Ch5.8
sends data to dynamic
triggers dynamic
Design guidance
- SHOULD use confidence scoring to prevent retrieval of marginal matches that add noise without information.
- SHOULD, when retrieval confidence is too low, pass no context and let the agent acknowledge that relevant information is unavailable.
Quantitative guidance
As stated by the sources; verify before use.
- Worked example keeps chunks with similarity >= 0.84 (top 3 of 5) to keep LLM context concise (Ch5.8).
Classification
- Patterns
- Confidence-thresholded retrieval
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Cost efficiency
- Risks mitigated
- Noise from marginal matchesHallucination induced by irrelevant retrieval
Sources
- Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.
- Ch5.8: T. Nguyen, "Semantic Memory," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.8. ISBN: 9798244538229.
- Ch6.2A: T. Nguyen, "Vector Database Selection," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.2A. ISBN: 9798244538229.