🔁 Feedback fuels evals first, fine-tuning second
PM AI Feedback Loops
(2026 Edition)
5 feedback signals and 4 practices for AI product PMs.
Build AI Feedback PM Skills — Free →5 Signals
1.
Thumbs up/down — easy but shallow
2.
Edits to AI output — high signal, low friction
3.
Acceptance rate of suggestions — implicit signal
4.
Re-rolls and retries — what didn't work
5.
Long-form feedback for power users
4 Practices
1.
Capture but don't over-prompt — feedback fatigue is real
2.
Aggregate before acting — single user feedback is noise
3.
Close the loop — show users their feedback was heard
4.
Use feedback for evals first, fine-tuning second
FAQ
How important is RLHF for AI products?
Important for big foundation labs. Less critical for application-layer products that can use prompt engineering, fine-tuning, and eval-driven iteration. Most PMs don't need to do RLHF; they need solid feedback capture and an eval suite that improves with feedback.