Observability & Evaluation · Data artifact

Holdout Evaluation Set

Data artifactObservability & EvaluationObservability & Evaluationarc:HoldoutEvaluationSet

A sequestered evaluation dataset never consulted during configuration exploration, used only once configuration decisions are final to measure generalisation.

Responsibility. Provides an unbiased generalisation estimate for finalised configurations.

Also known as: Holdout Set, Final Evaluation Set, Rule validation dataset, Validation set

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Direct neighbourhood (hover for relationship types)

Relationships

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Design guidance

Quantitative guidance

As stated by the sources; verify before use.

Classification

Quality attributes
Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Risks mitigated
Information leakage from test set into configuration selection

Sources

  1. Ch3.4: T. Nguyen, "Tuning Model Parameters for Production Performance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.4. ISBN: 9798244538229.
  2. Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. ISBN: 9798244538229.
  3. Ch5.11: T. Nguyen, "Rule-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.11. ISBN: 9798244538229.
  4. Ch7.5: T. Nguyen, "NeMo Curator, Riva Speech AI & Multimodal Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.5. ISBN: 9798244538229.
  5. Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. ISBN: 9798244538229.
  6. Ref6.01: S. Schürch, "How to Make Your LLM More Accurate with RAG & Fine-Tuning," Towards Data Science, Mar. 11, 2025. [Online]. Available: https://towardsdatascience.com/how-to-make-your-llm-more-accurate-with-rag-fine-tuning/