AI data preparation makes the information used by a model suitable for its intended task. More records do not necessarily improve results when labels, units or access rights are inconsistent.
Define the decision the data will support and inspect representative records. Check missing values, duplicates, inconsistent labels and changes in how the data was collected. Record the origin and allowed use of material before importing it into a model workflow.
Separate development data from evaluation examples so that the test measures performance on cases the system has not already learned from or been tuned against. For document retrieval, preserve source identifiers and revisions so that an answer can be checked against the correct text.
Document transformations and review their effect on important groups and edge cases. Keep a process for correcting source errors and rebuilding derived datasets. Data preparation is an ongoing control when the business process and its inputs change.
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
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Reading adjacent decision areas keeps the topic from becoming an isolated note.