Review Training Data

Training data is the collection of examples used during training to adjust a model's parameters and shape its behavior on a defined objective.

The examples a model learns from influence which patterns it can recognize and reproduce. Data quality, coverage, labeling, provenance, and representativeness all matter alongside the training method.

Training data can introduce useful knowledge as well as gaps, bias, legal risk, and outdated assumptions. It is one major contributor to behavior, not the only one.

A document classifier is trained on earlier messages labeled by category so it can classify new messages.

Training data is not the single explanation for every output. Architecture, objectives, fine-tuning, prompts, retrieval, tools, and deployment controls also influence behavior.

Training data supplies examples from which the model learns patterns.

* [[machine-learning|Machine Learning]] * [[data-provenance|Data Provenance]] * [[bias|Bias]] * [[synthetic-data|Synthetic Data]] * [[capability-provenance|Capability Provenance]]

* [NIST AI 100-2 E2025, Adversarial Machine Learning](https://doi.org/10.6028/NIST.AI.100-2e2025) — NIST terminology for training data and threats affecting it. * [NIST AI 100-1, Artificial Intelligence Risk Management Framework](https://doi.org/10.6028/NIST.AI.100-1) — governance and measurement guidance for data used across the AI lifecycle.

* Trace origins with [[data-provenance|Data Provenance]] and downstream effects with [[bias|Bias]].