Lesson 7 · 11 min
The data flywheel: production logs → training data
Production logs are the highest-signal training data source. Building the pipeline that converts real user interactions into a continuously improving training set is what separates one-shot fine-tunes from compound improvement.
What a data flywheel is
A data flywheel is a self-reinforcing loop:
- Deploy a model to production
- Collect the inputs, outputs, and quality signals (user feedback, downstream metrics)
- Convert high-signal interactions into labeled training examples
- Fine-tune the next model version on this real production data
- Deploy the improved model → go to step 2
Each turn of the flywheel produces a better model, which generates better interactions, which produce better training data. The compound improvement is what makes well-resourced AI products hard to catch up to — they have years of flywheel turns.
For a small team: even a simple version of this loop (log failures → hand-label 50 → retrain monthly) produces measurable improvement.