Start with the task, not the dataset.

A data brief often begins with a quantity: a number of examples, a volume of tokens, a delivery date. Those constraints matter, but they cannot tell us whether the material will be useful. For Silo Data, the starting question is what someone expects the model to accomplish.

Consider a request to review a technical document. Success could mean detecting contradictions, checking compliance with a specification, or explaining a design to a new reader. Each interpretation implies different examples and different evaluation criteria. Treating them as one generic task makes the resulting signal difficult to interpret.

A better brief describes the available inputs, the decisions the system must make, and the conditions that determine completion. It also records what remains outside the task. These boundaries help reviewers apply a consistent standard and make disagreements easier to investigate.

Only then does format become the central question. Demonstrations may help establish the expected behavior. Comparisons may clarify a quality distinction. A tool environment may be necessary when the outcome depends on actions rather than prose.

This is the discipline we want to bring to data work: every example should connect to a purpose, and every evaluation should connect back to that purpose. More material is useful when it improves coverage of the task we actually care about.