User Feedback Loops for AI Products
How users interact with AI output is your best source of quality improvement signal. Designing feedback loops is an AI PM superpower.
The two types of feedback
AI products can collect feedback in two ways:
Explicit feedback: The user actively tells the AI it was right or wrong. 'This suggestion is helpful' / 'Mark as incorrect' buttons.
Implicit feedback: The AI infers from user behavior whether its output was useful. Did the user accept the suggestion? How quickly? Did they modify it after accepting? Did they delete it?
At BoostDraft, both apply. Explicit: the user clicks 'Dismiss' on a proofreading flag. Implicit: the user ignores a suggestion for 10 minutes, then manually fixes the same issue the AI flagged — a strong signal that the suggestion was not surfaced effectively.
A kikashi is a forcing move — it causes your opponent to respond in a way that benefits you. Good feedback loops work like kikashi: they force the AI to respond to user behavior, automatically steering it toward better performance.
Designing the feedback loop at BoostDraft
For each AI-powered feature in BoostDraft, the PM should design:
What signal do we capture? - Accepted suggestions (strong positive signal) - Dismissed suggestions (negative signal — but also useful: which types get dismissed most?) - Modified-after-accept (signal that the suggestion was directionally right but not quite) - Time-to-action (fast accept = high trust; slow accept = friction or uncertainty)
How does signal flow into model improvement? - Define how feedback data gets labeled and stored - Set a cadence for model retraining with new signal (weekly? per-release?) - Decide what volume of feedback triggers a retraining cycle
How do we close the loop with users? - Do users know their feedback is improving the product? This increases feedback participation. - Do we surface 'improved since last time' to users who gave feedback? Closes the loop explicitly.
Feedback loop pitfalls
Feedback loops can go wrong in specific ways:
Survivorship bias: You only see feedback from users who kept using the product. Users who found the AI unhelpful and churned gave you no feedback — but they're the signal you needed most.
Reinforcing bad patterns: If early users tend to accept a specific type of incorrect suggestion (because they trust the AI too much), the feedback loop reinforces the wrong behavior.
Data sparsity on edge cases: The feedback loop improves common cases quickly, but rare edge cases never get enough signal to improve.
Label noise: User dismissals aren't always quality signals. A user might dismiss a correct suggestion because they're in a hurry, or accept an incorrect one because they didn't read it carefully.