The data flywheel
The idea: Production traffic → log it → curate the hard and failed cases → label them → feed evals and training → a better model → more usage. The loop compounds, and your proprietary data becomes the moat.
What you'll be able to do: You can explain the data flywheel and why production data is now the key advantage.
The problem it solves: Two teams use the same base model. Why does one keep pulling ahead?
Builds on: Evals: proving it works, Datasets, labeling & ground truth
← Datasets, labeling & ground truth · Next: The lethal trifecta →
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