Data Generation Pipelines

A data generation pipeline is the infrastructure that turns a behavioral intent — “we want the model to handle billing questions more gracefully” — into a set of actual examples the model can learn from. Pipelines can include steps like generating draft examples with a model, filtering out low-quality outputs, routing items to human reviewers, and formatting everything for training. Building good pipelines is often unglamorous but critical work: a leaky or inconsistent pipeline can quietly degrade the data that shapes model behavior. Behavior architects often work closely with data and engineering teams to design pipelines that reflect behavioral goals, not just throughput targets.