Data Flywheels

Create closed-loop systems where deployed models generate training data from real-world usage, which is filtered and used to train improved models — enabling continuous, self-sustaining improvement. A data flywheel connects deployment back to training: a model is deployed, serves users, logs its interactions (queries, completions, user feedback), the logged data is filtered and cleaned, and the cleaned data is used to train the next model version. The key components are data logging infrastructure, quality filters (feedback signals, raters, automated checks), and regular retraining cycles. The flywheel compounds over time — better models generate better data, which trains even better models.