Data quality before training an AI model

An evaluation result can hide a data problem

A company collects examples, trains a model and gets a good score. But related documents can appear in both training and test data. The evaluation then measures repeated content as well as the ability to answer new situations.

The infrastructure must preserve each example's source and the version used. Without this link, it is difficult to explain a regression, remove an unsuitable source or repeat an evaluation after a correction.

Separate groups as well as rows

In a platform I implemented, the split uses a group identifier and a stable configuration. Examples from the same group stay in the same training, validation or test partition. This prevents a type of contamination that a random row split can introduce.

The group must represent the relationship that matters to the product. It can be a document, a source or a service sequence. A stable identifier cannot resolve relationships that data preparation failed to record.

Define what blocks a run and what needs review

The implementation also separates problems that block a version from warnings that need review. A missing partition and an unrepresentative dataset have different consequences. The operator must know which condition failed and which version received the decision.

These controls make the process verifiable. They do not prove response quality or replace an evaluation suited to the domain. The team must define reference examples and acceptance criteria before comparing models.

What consulting can deliver

A review can map sources, versions, groups, duplicates and points where information is lost. Deployment can include data preparation, eligibility criteria and evaluation records. The scope follows the model's purpose and the permitted data access.