What is a data flywheel for an AI product, and how do you design feedback collection?

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A data flywheel is the loop where product usage generates data (feedback, corrections, outcomes) that improves the system (better prompts, retrieval, evals, fine-tuned models), which attracts more usage. Design it with explicit feedback (thumbs, ratings, corrections), implicit signals (accept/edit/copy, retries, abandonment, task completion), traceable logs linking feedback to the exact prompt/model version, and a pipeline that turns feedback into labelled eval and training data.

Users generate logs, traces and feedback into a feedback store; triage and labelling grow eval sets and training data, which improve prompts, retrieval, models and routing, released through A/B tests back to users.

Signals

Explicit	Implicit
👍 / 👎, star ratings	Suggestion accepted vs dismissed (copilots)
Written correction	Edit distance between AI draft and sent version
"Report a problem"	Regenerate clicks, rephrased follow-ups
Escalation to human	Task completed, conversion, retention

Design principles

Make feedback low-effort and contextual (one click).
Link feedback to the trace ID and versions, or it's not actionable.
Account for bias: explicit feedback is sparse and skewed toward angry users. Combine it with sampled human review.
Privacy: consent and policies for using customer data to improve models; enterprise customers often forbid training on their data.

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What is a data flywheel for an AI product, and how do you design feedback collection?

Signals

Design principles

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