One Dataset Fits All? Why Training, Eval, RAG & Agent Data Must Be Separate
The article explains why AI systems need four distinct data products—training sets, evaluation sets, RAG knowledge bases, and Agent contexts—each with separate purpose, structure, timeliness, isolation, and acceptance criteria, warning that reusing a single dataset creates false quality metrics and operational risks.
Many enterprises recognize that training, evaluation, RAG, and Agent systems need different data. Yet in practice this is often reduced to copying one batch of data into separate directories for training, evaluation, vector stores, and Agent knowledge bases. Physical separation does not equal proper data product division.
When data purposes are not separated, several problems appear simultaneously:
Training and evaluation sets contain duplicate or near-duplicate samples, making model metrics look good while production performance drops.
New and obsolete regulations enter the vector store together; RAG retrieves content but cannot judge which version is still valid.
Current contract status is periodically copied into a knowledge base, so Agents may read stale facts instead of the latest business-system state.
Agents can retrieve large volumes of material but do not know what the current user is allowed to see or which actions require approval.
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